analysis and classification of sounds produced by asian
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Portland State University Portland State University
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Dissertations and Theses Dissertations and Theses
5-5-2009
Analysis and Classification of Sounds Produced by Analysis and Classification of Sounds Produced by
Asian Elephants (Asian Elephants (Elephas MaximusElephas Maximus) )
Sharon Stuart Glaeser Portland State University
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Recommended Citation Recommended Citation Glaeser, Sharon Stuart, "Analysis and Classification of Sounds Produced by Asian Elephants (Elephas Maximus)" (2009). Dissertations and Theses. Paper 4066. https://doi.org/10.15760/etd.5951
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THESIS APPROVAL
The abstract and thesis of Sharon Stuart Glaeser for the Master of Science in
Biology were presented May 5, 2009, and accepted by the thesis committee and the
department.
COMMITTEE APPROVALS:
RandyZeli~
Luis Ruedas
<::;;>
Thomas Dolan
David Shepherdson
DEPARTMENT APPROVAL:
Michael Murphy, Chair Department of Biology
ABSTRACT
An abstract of the thesis of Sharon Stuart Glaeser for the Master of Science in
Biology presented May 5, 2009.
Title: Analysis and classification of sounds produced by
Asian elephants (Elephas maximus)
Relatively little is known about the vocal repertoire of Asian
elephants (Elephas maximus), and a categorization of basic call types and
modifications of these call types by quantitative acoustic parameters is needed to
examine acoustic variability within and among call types, to examine individuality,
to determine communicative function of calls via playback, to compare species and
populations, and to develop rigorous call recognition algorithms for monitoring
populations.
This study defines an acoustic repertoire of Asian elephants based on
acoustic parameters, compares repertoire usage among groups and individuals, and
validates structural distinction among call types through comparison of manual and
automated classification methods. Recordings were made of captive elephants at
the Oregon Zoo in Portland, OR, USA, and of domesticated elephants in Thailand.
Acoustic and behavioral data were collected in a variety of social contexts and
environmental noise conditions. Calls were classified using perceptual aural cues
plus visual inspection of spectrograms, then acoustic features were measured, then
automated classification was run. The final repertoire was defined by six basic call
types (Bark, Roar, Rumble, Bark, Squeal, Squeal, and Trumpet), five call
combinations and modifications with these basic calls forming their constituent
parts (Roar-Rumble, Squeal-Squeak, Squeak train, Squeak-Bark, and Trumpet
Roar), and the Blow. Given the consistency of classifications results for calls from
geographically and socially disparate subject groups, it seems possible that
automated call detection algorithms could be developed for acoustic monitoring of
Asian elephants.
2
1ANALYSIS AND CLASSIFICATION OF SOUNDS PRODUCED BY
ASIAN ELEPHANTS (ELEPHAS MAX/MUS)
by
SHARON STUART QLAESER
A thesis submitted in partial fulfillment of the requirements for the degree of
MASTER of SCIENCE in
BIOLOGY
Portland State University 2009
Dedication
This thesis is dedicated to my dear husband, Bobby, who
offered his love and support throughout this adventure and
encouraged me to share my heart with elephants.
Acknowledgements
First and foremost I would like to thank my thesis committee for their
expertise and guidance: Dr. Randy Zelick, Dr. Luis Ruedas, Dr. Thomas Dolan,
Dr. David Shepherdson, and Dr. David Mellinger. I am especially thankful to my
advisor, Dr. Zelick, who met the challenges of data collection with enthusiasm and
was so patient with me. I am eternally grateful to the Oregon Zoo elephant handlers,
without whom this study would not have been possible. I especially appreciate the
support from those most involved, April Yoder, Bob Lee, Joe Sebastiani, Jeb Barsh,
and Dimas Dominguez. Dr. Mellinger and Dr. Bolger Klinck, with the OSU/NOAA
Cooperative Institute for Marine Resource Studies, provided invaluable supervision
and support with acoustic analysis at a critical time. I appreciate the support and
encouragement from the Oregon Zoo Conservation Program staff, Anne W amer,
David Shepherdson, and Karen Lewis, and Deputy Director Mike Keele. I thank Dr.
Mandy Cook for insight and guidance with early analysis, and Jean Lea Hofheimer
and Bobbi Estabrook for scoring calls. I thank the managers at the Royal Elephant
Kraal and Elephant Nature Park for the opportunity to record domesticated elephants
in Thailand. Travel funding was granted by Portland State University Marie Brown
and Academically-Controlled Auxiliary Activities Fund. I thank my loving family,
Meghan Martin, and other dear friends who embraced this project with me. Finally, I
am eternally grateful to Nancy Scott and the late Dr. LEL "Bets" Rasmussen for
welcoming me into the elephant research community and paving the road, and to the
many elephant handlers, veterinarians, and elephants who have taught me so much.
ii
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ........................................................................................... ii
LIST OF TABLES ......................................................................................................... v
LIST OF FIGURES ....................................................................................................... vi
CHAPTER I: INTRODUCTION ................................................................................... 1
TAXONOMY AND CONSERVATION STATUS ................................................................... 2
ECOLOGY AND COMMUNICATION ................................................................................ 4
VOCAL PRODUCTION AND INDIVIDUALITY IN ACOUSTIC SIGNALS ................................ 6
Low-FREQUENCY SOUNDS ........................................................................................... 9
HEARING .................................................................................................................... 11
PURPOSE AND SIGNIFICANCE OF THIS THESIS RESEARCH ............................................ 12
CHAPTER II: PUTATIVE CALL TYPES AND MANUAL CALL CLASSIFICATION ...................................................................................................... 16
METHODS .................................................................................................................. 16
RESULTS .................................................................................................................... 21
CHAPTER III: CALL DISTRIBUTION AND CALL RA TE - HOW THE REPERTOIRE IS USED BY GROUPS AND INDIVIDUALS .................................. 27
METHODS .................................................................................................................. 27
RESULTS .................................................................................................................... 29
CHAPTER IV: ACOUSTIC PARAMETERS AND AUTOMATIC CLASSIFICATION OF CALLS .................................................................................. 33
METHODS .................................................................................................................. 33
RESULTS .................................................................................................................... 53
lll
CHAPTER V: DISCUSSION ...................................................................................... 78
REFERENCES ............................................................................................................. 82
APPENDIX A: DISTRIBUTION PLOTS OF PARAMETERS .................................. 87
HISTOGRAMS AND BOXPLOTS OF ORIGINAL DATA ...................................................... 87
Q-Q PLOTS OF SCALED DAT A .................................................................................... 104
Box PLOTS OF SCALED v ARIABLES FOR IDENTIFYING OUTLIERS .............................. 111
APPENDIX B: CLASSIFICATION ON TREE RAW DAT A OUTPUT ................. 118
APPENDIX C: PRINCIPAL COMPONENT ANALYSIS DAT A - RAW DATA OUTPUT .................................................................................................................... 123
APPENDIX D: ANALYSIS OF SIMILARITY (ANOSIM) DATA OUTPUT ........ 124
APPENDIX E: POWER SPECTRA OF RUMBLES AND NOISE .......................... 127
lV
LIST OF TABLES
Table 1: Acoustic ethogram of putative call types showing single calls, trains of calls, and call combinations ........................................................................................... 22
Table 2: Social contexts for call comparison ................................................................ 28
Table 3: Acoustic parameters measured by Osprey ...................................................... 38
Table 4: Calls in dataset for statistical analysis (N=927) ............................................ .42
Table 5: Acoustic parameters that were used in statistical analysis ............................ .45
Table 6: Calls in dataset (N=908) after outliers removed ............................................ .49
Table 7: Confusion matrix for tree ................................................................................ 59
Table 8: Classification rate(%) for tree ........................................................................ 59
Table 9: Summary of pair-wise ANOS IM showing significant difference between all call types ............................................................................................................... 66
Table 10: Median values of acoustic parameters shown by the classification tree and PCA to differentiate basic call types ..................................................................... 67
Table 11: Final ethogram for Bark, Roar, Rumble, Squeak, Squeal, Trumpet, and Blow ...................................................................................................................... 68
v
LIST OF FIGURES
Figure 1: Call distribution of the Oregon Zoo herd versus domesticated elephants in Thailand ................................................................................................................ 29
Figure 2: Call distribution of females in the Oregon Zoo herd .................................... 30
Figure 3: Call distribution of one male (Tusko) in the Oregon Zoo herd .................... 31
Figure 4: Mean call rate as a function of social context ...................... ." ........................ 32
Figure 5: Osprey window of Squeak with annotation box and feature box ................. 34
Figure 6: Osprey window of Trumpet with annotation box and feature box ............... 35
Figure 7: Acoustic parameters measured by Osprey that can be visualized ................ 3 7
Figure 8: Boxplot of scaled variables across all call types (blow, bark, roar, rumble, squeak, squeal, trumpet) ....................................................................................... 43
Figure 9: Scaled variables for Roar showing outliers and gradients ..................... .48
Figure 10: Example histograms and boxplots of acoustic parameters for each call type show distribution and variability .......................................................................... 55
Figure 11: Final classification tree model .................................................................... 58
Figure 12: Principal Component Analysis plot for basic call types .......................... 61
Figure 13: Global ANOSIM test for difference in parameters among the call types ... 65
Figure 14: Power spectra of Rumble and background noise at the Oregon Zoo ........ 75
Figure 15: Power spectra of Rumble and background noise at Elephant Nature Park ................................................................................................. 76
Figure 16: Power spectra of rumbles and background noise at the Royal Elephant Kraal ......................................................................................... 77
VI
CHAPTER I: INTRODUCTION
Relatively little is known about the vocal repertoire of Asian elephants
(Elephas maximus). Our knowledge of acoustic communication in elephants is based
primarily on research on three wild African elephant (Loxodonta spp.) populations
(Amboseli National Park in Kenya, Etosha National Park in Namibia, central African
forests), three captive African elephant herds (Disney's Animal Kingdom in Lake
Buena Vista, Florida, U.S.A., Vienna Zoo in Austria, and Daphne Sheldrick's
orphanage in Nairobi National Park in Kenya), one wild Asian elephant population
(Gal Oya National Park in Sri Lanka), and one captive Asian elephant herd (Oregon
Zoo in Portland, OR, USA, formerly Washington Park Zoo). Additional elephant
populations are currently under study, but publications are limited.
Over 30 calls have been published for African elephants (Loxodonta spp.)
(Olson, 2004; Stoeger-Horwath et al., 2007), and nine have been published for Asian
elephants (Elephas maximus) (Olson, 2004; McKay, 1973); but most are described
only by context or function and not by acoustic features. Among the published African
elephant calls, six infant calls have been described by both spectral and temporal
features in addition to context and function, so there is advancement in our
understanding of vocal ontogeny (Stoeger-Horwath et al., 2007). However, less than
16 calls produced by sub-adults and adults have been described by temporal or
spectral features, and most of these appear to be variations of the low frequency
rumble that differ in social context and function (Olson, 2004; Langbauer, 2000).
Among the published Asian elephant calls, only the low frequency rumble has been
1
described by spectral and temporal features. African elephants are capable of
producing calls ranging from 5 Hz to 9 kHz (Poole, in prep). Infrasonic calls (below
20 Hz) have been recorded from captive Asian elephants (Payne et al., 1986), but the
frequency range of Asian elephant vocalizations is unknown.
TAXONOMY AND CONSERVATION STATUS
Elephants belong to the order Proboscidea and family Elephantidae. The two
extant genera of the family Elephantidae include the African elephants (Loxodonta
africana, Blumenbach 1769) and the Asian elephants (Elephas maximus, Linnaeus
1758).
Subspecies taxonomy of Elephas maximus varies among authors, but the
International Union for Conservation of Nature and Natural Resources (IUCN)
follows Shoshani and Eiesenberg ( 1982) in recognizing three subspecies: E. M.
indicus on the Asian mainland (India and Indochina), E. m. maximus on Sri Lanka,
and E. m. sumatranus on the Indonesian island of Sumatra (IUCN, 2008). These
subspecies designations were based primarily on morphological differences, but
mtDNA variation suggests that E. m. sumatranus is monophyletic (Fleischer et al.,
2001). Borneo's elephants are not currently listed as a subspecies and their origin is
controversial; however, comparison of mtDNA of Borneo elephants to that of Asian
elephants across their range suggest that Borneo's elephants are genetically distinct,
with divergence indicative of a Pleistocene colonization of Borneo (Fernando et al.,
2003). Given genetic distinction, the IUCN could define these taxons as evolutionarily
significant units (ESU) (IUCN, 2008).
2
The IUCN uses Sukumar's (2003) estimate of 41,410-52,345 wild elephants
for the global population, but acknowledges the argument by Blake & Hedges (2004)
that these estimates may be inaccurate due to the challenges of surveying forest
dwelling populations. A more recent estimate lists a wild population of 38,534-52,566
animals (Sukumar, 2006) and a global captive population of approximately 16,000
(Sukumar, 2006; Fischer, 2004). The Asian elephant is listed as Endangered by the
IUCN (Choudhury et al., 2008. Elephas maximus. In: IUCN 2008) and is listed in
Appendix I of the Convention on International Trade in Endangered Species of Wild
Fauna and Flora (CITES) (CITES, 2008). The greatest threat to the Asian elephant is
habitat loss, degradation, and fragmentation driven by an expanding human
population, which leads to increasing conflicts between human and elephant. Poaching
for ivory and other body parts also pose a major threat to the long-term survival of
some populations. Large-scale hunting has reduced populations significantly over
wide areas; but even selective removal of tusked males can result in skewed adult sex
ratios and reduced genetic variation, especially in areas where populations are isolated
(Choudhury et al., 2008. Elephas maximus. In: IUCN 2008).
Although there is genetic evidence that suggests there may be at least two
species of African elephants, namely the African savanna elephant (loxodonta
africana) and the African forest elephant (Loxodonta cyclotis) (Roca et al., 2001), both
the IUCN and CITES classify all African elephants as a single species encompassing
both savanna and forest populations (IUCN, 2008; CITES, 2008). Many members of
the international.elephant management and medical community currently recognize
3
the reclassification of Loxodonta africana into two separate species, Loxodonta
africana and Loxodonta cyclotis, with the following subspecies of Loxodonta
africana, L.a. africana (South African bush elephant), L.a. Knochenhaueri (East
African bush elephant), and L.a. oxyotis (West African bush elephant).
The African elephant is listed as Near Threatened by the IUCN (Blanc, 2008.
Loxodonta ajricana. In: IUCN 2008) and is in Appendix I of CITES, with four
populations down-listed to Appendix II (CITES, 2008). Poaching for ivory and meat
remains a significant threat, but the most important perceived threat is habitat loss and
fragmentation caused by an expanding human population and land conversion, which
in tum leads to an increase in human-elephant conflict (Blanc, 2008. Loxodonta
africana. In: IUCN 2008).
ECOLOGY AND COMMUNICATION
Elephants are intelligent, long-lived animals that live in a complex and fluid
society in which several modes of communication play a role in maintaining group
cohesion and social order, and in locating and assessing reproductive state of potential
mates (Langbauer, 2000; Eisenberg et al., 1971). Asian and African elephants have a
similar family structure. Females live in matriarchal herds consisting of other female
relatives and their young. Elephant society is multi-tiered, with family units joining to
form bond groups or larger clans that share a home range and coordinate movements
(Douglas-Hamilton, 1972; Charif et al., 2005). Males are excluded from familial units
at a young age and live as solitary elephants or in temporary association with other
males in "bachelor herds" at the fringes of the female herds (Eisenberg et al., 1971;
4
Poole, 1987; Vidya & Sukumar, 2005). Recent studies suggest that bull society may
be more structured than previously thought, and that young bulls may seek the
company of mature bulls (Kate Evans, pers. comms). There is evidence that larger
clans may not form in Asian elephant society, so ranging behavior may be influenced
to a lesser degree by extended kin compared to African savannah elephants (Fowler &
Mikota, 2006).
Data on Asian elephant mating behavior in the wild are scarce and incomplete,
but studies of African savannah elephants suggest that mate choice by females and
mate guarding by males appear to play crucial roles in the reproductive behavior of at
least the African savannah species (Moss, 1983; Poole, 1989).
Infrasonic vocalizations and chemical signaling are considered primary
modalities for long-distance communication (Payne et al., 1986; Poole, 1999);
whereas visual, tactile, auditory, and chemical signals are all important at close range
(Schulte & Rasmussen, 1999). Recent findings with seismic detection and
discrimination suggest that vocalizations traveling in the seismic channel could be
used for both short- and long-distance communication (0' Connell-Rodwell et al.,
2006; O'Connell-Rodwell et al., 2007). Acoustic signals are temporally short-lived and
both sender and receiver must be present for the signal to be communicated, and thus
these signals provide information only on an immediate situation, for example,
location (Langbauer, 2000). Chemical signals are temporally long-lived and the sender
and receiver need not both be present for the signal to be communicated, and thus ·
5
these signals provide information on a constant state, for example, reproductive state
(Langbauer, 2000).
VOCAL PRODUCTION AND INDIVIDUALITY IN ACOUSTIC SIGNALS
Mammals can modify certain basic sounds by changing the amplitude,
temporal patterning, or stressing of overtones (Muckenhim in McKay, 1973). Vocal
individuality has been found in other mammalian species, for example bottlenose
dolphins (Tursiops truncatus) (Janik et al., 2006), rhesus macaques (Macaca mulatta)
(Rendall et al., 1998), fallow deer (Dama dama) bucks (Reby et al., 1998), swift fox
(Vulpes velox) (Darden et al., 2003), but it is unknown how or if individual identity is
communicated via the acoustic channel in Asian elephants. Strnctural variation within
a call may serve a communicative function (Green, 1975 & Bayart et al., 1990 in
Soltis et al., 2005), but call structure must first be characterized.
Characteristics of vocalizations that arise from inherent properties of vocal fold
vibration in the larynx (the source) vary independently from properties that arise from
vocal tract resonance (the filter), so either the source or filter may provide the auditory
characteristics a receiver needs to determine identity (McComb et al., 2003 ). Air
driven from the lungs sets the vocal folds in motion. Vibration of the vocal folds in the
larynx has afundamentalfrequency and harmonics. In terrestrial mammals, the
fundamental frequency is determined by how many times the vocal folds vibrate in
one second, measured in cycles per second or Hertz; and the fundamental frequency is
negatively correlated to vocal cord mass (Fitch, 1997). The fundamental frequency
determines the pitch that we hear. Harmonics of sound waves are integer multiples of
6
the fundamental frequency; that is, if the fundamental frequency is 25 Hz, then the
harmonics have frequencies of 25 Hz, 50 Hz, 75 Hz, etc. The human auditory system
does not perceive separate harmonics in sound, but harmonics provide the tone quality
or richness of the sound.
The source sound from the larynx passes through the vocal tract, which
selectively amplifies certain frequencies, and thus filters the spectral envelope to
produce peaks calledformants (McComb et al., 2002). As vocal tract changes shape,
its resonance frequencies change, and different formants are produced. Articulators,
t.he tongue and lips, produce more complex configurations of the vocal tract and
hence more complicated formant patterns (Baken, 1996). The average spacing
between consecutive formants, or formant dispersion, can provide information on the
length of the vocal tract in mammals (McComb et al., 2003), with an increase in vocal
tract length resulting in a decrease in formant spacing in many mammalian species
(Sanvito et al., 2007; McComb et al., 2003; Fitch & Hauser, 2002).
The sound energy received is a factor of sound production intensity and
acoustic propagation loss in the sound channel, which is influenced by topography,
boundary conditions (surface and vegetation), and atmospheric structure (temperature,
humidity, pressure) (Garstang, 2004).
The vocal tract of an elephant is the nasal passages of the skull, the trunk, and
the pharyngeal pouch (Garstang, 2004). The pharyngeal pouch is at the posterior one
third of the nasopharynx (Fowler & Mikota, 2006). The elephant tongue is unable to
protrude from the mouth due to the attachment from the tip of the tongue to the floor
7
of the mouth (Fowler & Mikota, 2006), so the tongue is limited in the articulation of
sounds.
Adult Asian elephants have a size range of 2,000-5,500 kg (4,410-12,125 lbs)
and 2-3.5 meters (6'7"-11'6") at the shoulder (Shoshani, 2000), and there is a visible
difference between individuals in the size of the head and trunk, or the vocal tract.
Center frequencies of formants depend on the size and posture of the caller, and
different individual African savannah elephants have been found to overlap in these
frequencies (McComb et al., 2002). However, there has been no comparative work
done on the size of the vocal folds of elephants.
Soltis et al. 2005 (2005) found that structural variation in the rumble from
adult female African elephants housed at Disney Animal Kingdom (Lake Buena Vista,
Florida, U.S.A.) reflected individual identity and negative emotional arousal of caller.
In the presence of dominant females, subordinate females produced rumbles with low
tonality and unstable pitch compared to rumbles produced outside the presence of
dominant females. Results of acoustic playback studies with African savannah
elephants on female recognition of the contact call (McComb et al., 2003) and on
signal assessment of the musth rumble (Poole, 1999) suggest individual- and size
related differences in acoustic production, which elicit questions regarding
individualism, social recognition, and size assessment. Adult females were able to
discriminate familiar and unfamiliar contact calls, and it appeared that the fundamental
frequency contour extracted from the harmonics was the key characteristic in the
signal that could be used for distinguishing individual calls at a distance (McComb et
8
·--1
al., 2000; McComb et al., 2003). Adult males responded to playback of musth rumbles
in a way that suggests they are able to assess characteristics of the caller in this
acoustic signal, but it is unclear whether they assess size in the signal or whether they
recognize the caller. The musth rumble is a low-frequency vocalization produced only
by males in musth, and is shown to advertise the musth state (Poole, 1987; Poole,
1989). The musth rumble has been studied extensively in the African elephant but not
in the Asian elephant. In response to the musth rumble of high-ranking males, other
musth males approached aggressively, while non-musth males walked away (Poole,
1999), which was consistent with prior studies of this population on the state of musth,
dominance, and a willingness to contest access to females (Poole, 1989). Elephants
are long-lived, intelligent animals, and they meet and interact over a period of
decades, so it is reasonable to hypothesize that males also recognize individuals by
their calls (Poole, 1999), and that they assess size based on prior interactions rather
than assessing the size of an individual by their call. To further investigate the
potential for size assessment in acoustic signals, the acoustic features that have the
potential to code for size (fundamental frequency and formant dispersion) need to be
measured for calls used over distances or barriers where visual access is limited.
LoW-FREQUENCYSOUNDS
lnfrasound is an anthropocentric term for describing frequencies below the
human hearing range, which is nominally 20 Hz to 20 kHz. The long wavelengths of
low-frequency sound are more likely to hit only large objects and be reflected, and are
thus resilient to atmospheric attenuation with distance (Langbauer et al., 1991; Larom
9
et al., 1997). Low-frequency sound below 100 Hz shows little attenuation in forest
environments (Marter et al., 1977; von Muggenthaler et al., 2001). fufrasonic
vocalizations of elephants have fundamental frequencies typically in the range
14 Hz to 35 Hz, with harmonics that extend into the audible range (Poole et al., 1988;
Payne et al., 1986; Langbauer et al., 1989; Langbauer et al., 1991). Elephants can
vocalize in the infrasonic range up to levels of 103 +/- 3 dB at 5m from the source
(Garstang et al., 1995). The role and production mechanism of infrasound varies
among species. Other species known to produce infrasonic sounds include the okapi,
Bengal and Siberian tigers, giraffe, and Sumatran rhino (von Muggenthaler, 1992; von
Muggenthaler, 2000; von Muggenthaler et al., 200 I; von Muggenthaler et al., 2003 ).
The hyoid apparatus of elephants differs from the basic mammalian scheme
and allows a greater flexibility of the larynx, which may aid in the production of
infrasound (Langbauer, 2000). This difference in the hyoid apparatus presents a
potential proximate cause for production of infrasound, and the need for long-distance
communication presents an ultimate cause. McComb (2002) suggested that elephants
produce fundamental frequencies in the infrasonic range simply because of their large
size rather than an evolved mechanism for long-distance communication·. McComb
(2002) analyzed the source- and filter-related acoustic features of the African elephant
female contact call in the Amboseli National Park population, measured degradation
of the spectral structure of this call with distance, and performed playback to measure
signal perception and assessment. Harmonics peaks around 115 kHz were most
prominent and had the highest persistence with distances of 0.5 km to 2.5 km. Female
10
groups showed signs of detecting calls from distances of 2 km to 2.5 km, but did not
respond as though they were familiar or unfamiliar until the distance narrowed to 1.0
or 1.5 km. These results suggest that although lower frequencies have the potential to
travel further, the most important frequency components for long distance
communication of social identity in African elephants may be in the harmonics around
115 Hz rather than in the infrasonic range of the fundamental frequency of the contact
call (mean 16.8 Hz) (McComb et al., 2002).
HEARING
There is some evidence that elephants may be better adapted for perceiving
acoustic signals in the 100 Hz to 4 kHz range than in the range of the lowest
frequencies produced. Heffner & Heffner (1982) measured the hearing sensitivity of a
captive Asian elephant, and found the elephant to have an audibility curve similar to
that of other mammals, but with a greater sensitivity to low frequencies and lower
sensitivity to high frequencies than any other mammal tested prior to this study. The
elephant's absolute threshold was 16 Hz (at 65 dB) to 12 kHz (at 72 dB), with a 17
Hz to 10.5 kHz hearing range by the 60 dB criterion. Although the elephant had a low
frequency threshold of 16 Hz, it was considerably less sensitive to frequencies below
100 Hz than to those between 100 Hz and 4 kHz, and the maximum sensitivity was
1 kHz (at 8 dB). From 31.5 Hz to 2 kHz the thresholds were close to background noise
level, so it is possible that the animal's actual sensitivity in this region was masked by
background noise. Frequency discrimination tests indicated the elephant's frequency
discrimination was best below 1 kHz.
11
Localization of sound relies on the time of arrival to the ear and the intensity of
sound reaching the ear. Because the interaural distance in the elephant is large, sound
reaches one ear long before the other, and the intensity of the sound on the ear closest
to the sou_nd is greater than on the opposite ear due to the buffering effects of the head.
The larger the interaural distance, the greater the ability to localize low frequency
sounds because the greater interaural distance means a larger phase and amplitude and
difference of the incoming sound wave between the two ears. Heffner & Heffner
(1982) conducted localization experiments for a frequency range of 125 Hz to 8 kHz,
source angles of 0° to 60°, and various stimuli. The elephant performed best at
localizing sound below 300 Hz and was virtually unable to localize 4 kHz to 8 kHz.
Pinnae extension appeared to play a role in localizing sound in this study
(Heffner et al., 1982).
PURPOSE AND SIGNIFICANCE OF THIS THESIS RESEARCH
The purpose of this research is to contribute to the basic science of elephant
communication and to conservation efforts. The specific goals are to (1) define an
acoustic repertoire of Asian elephants based on acoustic parameters, (2) investigate
how the repertoire is used by groups and individuals, (3) compare manual and
automated classification to validate structural distinction among call types, (4) provide
a basis for comparing acoustic communication among elephant species and
populations, and (5) explore the potential for using call parameters to develop an
automatic detector of Asian elephant calls for acoustic monitoring applications.
12
-----,
Communication is the intended transfer of information (a signal) from a sender
to either an intended or unintended receiver. Communication is intended to confer
some advantage to the sender, receiver or both via natural or sexual selection. This
study analyzes sounds produced by elephants, but there was no examination of an
intended transfer of information by measuring the response of conspecifics, so it is not
implied that these sounds are signals with communicative value. Data were collected
for future investigation of the communicative function of these sounds, but this topic
was not included in this analysis.
This study provides a basis for future research. A categorization of basic call
types and modifications of these call types by quantitative acoustic parameters is
needed to examine acoustic variability within and among call types, to examine
individuality, to determine communicative function of calls via playback, to compare
species and populations, and to develop rigorous call recognition algorithms for
monitoring wild and managed populations.
The task of vocalization classification and speaker identification is common in
bioacoustic analysis. Automated methods are particularly useful with large datasets or
when the repertoire is being compared between individuals or social groups (Deecke
& Janik, 2006). Detection algorithms are used extensively in the passive acoustic
monitoring of marine mammals, and there are many published classification methods
and tools that could be used with other vocalization datasets. Examples of methods
referenced for this current study include classification of African elephant and marine
mammals vocalizations. Campbell et al. (2002) used artificial neural network to
13
identify individual female Steller sea lions (Eumetopias jubatus). Clemins et al.
(2005) developed a Hidden Markov Model based on human speech recognition
techniques to automatically classify African elephant vocalizations for call type and
individual.
Classification of sounds by acoustic parameters is the first step in developing
call recognition algorithms for call detection and acoustic monitoring or census. Payne
et al. (2003) provides evidence that elephant calling patterns can be reliable indicators
of group size and composition, both of which are important for acoustic monitoring.
Calls were divided into three structures, single-caller low-frequency, single-caller high
frequency, and multiple-callers low-frequency. The rate of calling increased with
increasing numbers of elephants, and the distribution of these call categories changed
with group composition. Vocalizations may provide another tool for mitigating
human-elephant conflict, which often results in injury or death to both humans and
elephants (Kemf & Santiapillai, 2000). In Sri Lanka, researchers are investigating the
use of speech recognition techniques as a means of remotely identifying individual
elephants as they approach crops (Doluweera et al., 2003).
A library of vocalizations from known individuals in known reproductive
states will facilitate a more rigorous categorization of sounds by acoustic parameters,
and will allow an examination of sources of variability. An understanding of this
variability is needed to reliably determine the meaning of various calls via playback
(Langbauer, 2000). A database of acoustic communication of African savannah
elephants (Loxodonta africana) is currently being developed by the Savannah
14
Elephant Vocalization Project (SEVP) in Amboseli National Park (SEVP, 2008) and
by Disney's Animal Kingdom (John Lehnhardt, Joseph Soltis, pers comms). SEVP has
collected more than 70 different elephant call types of African savannah elephants and
linked them to observations of elephant behavior (SEVP, 2008), but the descriptions
of only a subset of these calls have been published. There is no similar database
known to be developed for wild or captive Asian elephants (Elephas maximus), so one
goal of this current study is to contribute to an animal communications database for
future research.
This study aims to provide a basis for comparisons of acoustic communication
in wild and captive Asian elephants, and could potentially serve as a basis for
comparisons between Asian and African elephants. These comparisons may provide
insights into the ecological role such communication plays and the requirements of
counterpart populations. As elephants lose habitat and find themselves under varying
degrees of management, the need to understand their requirements in captivity will
become even more important to the survival of the species (Riddle et al., 2003).
15
CHAPTER II: PUTATIVE CALL TYPES AND MANUAL CALL
CLASSIFICATION
METHODS
Study site and study subjects
Data were collected at the Oregon Zoo in Portland, Oregon, U.S.A. and in
Thailand. Data collected at the Oregon Zoo were from a captive community of three
male and four female Asian elephants. During the course of this study, the females
were managed as a single herd with only temporary separations. The males were
housed alone with no visual or physical access to other males; however, they did have
acoustic and olfactory access given their close proximity and the movement of
elephants between enclosures. Males and females were housed together during social
introductions and breeding. The Oregon Zoo elephant exhibit is designed with two
outside yards totaling 3 I 40 m2, and seven inside rooms. The Oregon Zoo is in an
urban setting located near a major interstate. Sources of anthropogenic noise include
highway traffic, air traffic, hydraulics, water, electric fences, zoo construction, a zoo
train, and visitors.
Data collected in Thailand were from two herds of domesticated elephants, one
herd of approximately 80 elephants at the Royal Elephant Kraal in urban Ayutthaya,
and one herd of approximately 30 elephants at the Elephant Nature Park in the rural
Mae Taeng Valley north of Chiang Mai. The Royal Elephant Kraal is a working
elephant village with adult and semi-adult bulls and cows, geriatric cows, and a
nursery of 10-12 calves. The Elephant Nature Park manages injured and abused
16
animals in a semi-captive setting where elephants are allowed to roam during the day
within the grounds and chained at night in social groups. At the time of recording,
there were three adult bulls, two sub-adult males, two calves, and females of varying
ages. Sources of anthropogenic noise in both Thailand sites were sporadic road noise,
machinery, water, and faint talking of visitors.
Acoustic data collection
· Data were collected at the Oregon Zoo from February 2005 to March 2009,
and consisted of over 80 hours of observations and recordings, with 56 hours of usable
data. Data were collected continuously for one hour (occasionally 30 min to 2 hours)
during social introductions, breeding events, temporary separations, arrival of a bull,
the death of a matriarch, novel events, and routine husbandry. TQe following data were
collected during each session: social context. behavior, vocalizations, and visual signs
of musth. Reproductive state measured by hormone levels was provided by the Oregon
Zoo. The caller, if identified during observation by sound localization or visual cues,
was dictated into the recorder or noted on a checksheet. One challenge was detecting
and localizing vocalizations at low frequency; however the harmonics often extended
into the audible range, making it possible to detect and localize at close proximity to
the caller.
Data were collected in Thailand from November to December 2009, and
consisted of approximately 6 hours of data. Data were collected continuously for one
hour sessions (or opportunistically for short sessions) during the morning release to
pasture, morning routines, night feedings, greetings between human and elephant, and
17
elephant painting. The following data were collected during each session: social
context, vocalizations, and focal animal if there was a focal.
Acoustic data were collected with four systems during the course of the study.
The frequency responses given for these systems are those provided by the
manufacturer, unless otherwise specified. The systems used were: (1) M-Audio
MicroTrack II (20 Hz -20 kHz,+/- 0.5 dB, recorder tested down to 10 Hz+/- 3 dB),
(2) Bruel & Kjaer 4145 condenser microphone (2.6 Hz -18 kHz+/- 2 dB), an ACO
type 012 preamp (flat to 0.5Hz), an ACO PS2000 power supply, and a Racal V-Store
24 Instrumentation Recorder (analog, DC-45.5 kHz), (3) Edirol R-09 mp3 recorder
(20 Hz - 40 kHz+/- 2 dB), (4) acoustic extraction from digital video (Panasonic PV
DV700, 20 Hz - 20 kHz) that is being used as part of an ongoing behavioral study.
Only the M-Audio and digital video were used to collect data in Thailand. Preliminary
data were collected with an loTech Wavebook 512 12-bit IMHz Data Acquisition
System, USBGear USB Sound card modified to record to DC, and Vetter 820 analog
recorder.
During recording sessions, the microphone was fixed in position with a tripod
to minimize extraneous noise from holding it directly, as per McComb (1996).The
distance to source was variable as subjects moved throughout the recording session, so
distance to source was only approximated for each recording session and there was no
attempt to measure absolute intensity of calls.
A Cambridge Electronic Design (CED) Micro1401 rnkII data acquisition unit
running Spike2 software (v5.12) was used to upload analog acoustic data. Adobe
18
Audition (vl.5) was used to convert file formats and split recording channels. Files
were first down-sampled to 25kHz to reduce the file size. The sampling rate was
selected based on the published frequency range of African elephant calls, 5 Hz to
9 kHz (Poole, in prep) which gives a minimum sampling rate at the Nyquist
frequency of 18 kHz. However, energy was found to extend above 12 kHz, so the
original sampling rate of either 32 kHz or 44 kHz was used for final measurements of
acoustic parameters.
Scoring calls and classifying calls into putative call types
The ethogram used for preliminary data collection included vocalizations
described by McKay (1973) and those in the Elephant Husbandry Resource Guide
(Olson, 2004). The ethogram of elephant behaviors in the Elephant Husbandry
Resource Guide is a compilation of ethograms from approximately 30 publications
and manuscripts, so it is a relatively comprehensive source. Ad libitum sampling was
employed to incorporate vocalizations not yet described. Six basic call types were
defined (trumpets, squeaks, squeals, roars, rumbles, and barks), and these call types
and modifications of these call types were fit into published nomenclature where
possible. Combination calls were also defined (trumpet-rumble, trumpet-roar, squeak
rumble, squeak-squeal, roar-rumble). Call descriptions were compared to those
recorded in an ongoing study in Uda Walawe National Park in southern Sri Lanka, and
a joint ethogram was developed for calls that were common to the captive herd at the
Oregon Zoo and the free-ranging elephants in Sri Lanka (Shermin de Silva, pers
comms.)
19
Exemplar calls were presented to elephant handlers and university students to
gather descriptions of how the sounds were perceived compared to familiar non
elephant sounds, with the aim of providing a description that would allow elephant
handlers and researchers to discuss elephant calls in terms of aural cues; for example,
a Squeal sounds like rubber shoes on a wood floor.
A signal or sound can be viewed as a distribution of energy in time and
frequency. These distributions can be represented several ways. A spectrogram
displays a three-dimensional plot that shows how the sound varies over time, with
frequency on the y-axis, time on the x-axis, and the relative power (or the logarithm of
relative power) at a given point in frequency and time represented as a darkness value.
A power spectrum displays average energy of the signal over a period of time, with
relative power on the y-axis and frequency on the x-axis (frequency domain). A
waveform displays amplitude on y-axis and time on x-axis (time domain).
Praat software ( v4.5.16, Institute of Phonetic Sciences of the University of
Amsterdam, The Netherlands) was used to annotate (score) sounds by adding
boundary markers and typing call notations (call type, caller ID, quality) in an
annotation field within the boundary. Calls were categorized into the putative call
types using perceptual aural cues and visual inspection of spectrograms for
differentiation of the following acoustic parameters: fundamental frequency contour
(start and end frequency, maximum and minimum frequency, inflection), tonality, and
signal duration. Categorizing by aural cues and visual inspection is considered a
manual method of classification. With the exception of blow sounds, every detectable
20
occurrence of every call type was scored. Environmental sounds were also scored for
future analyses with call detection. Half of the recordings were first scored by two
trained but inexperienced reviewers, then verified and corrected. Of 1243 sounds
scored by inexperienced reviewers using aural cues and spectrogram inspection, 90
required correction, for an interobserver reliability of 93%. The caller (if known) was
annotated for each call. A quality score of 1 to 4 was assigned to each call using
methods by Cambell et al. (2002), which were based on presence/absence of sporadic
sound overlap (elephant, human, environmental) and a subjective measurement of
degradation by ambient noise (e.g., highway, wind, water, electricity, faint sounds of
visitors, insects).
RESULTS
The ethogram of putative call types with descriptions and exemplar
spectrograms is provided in Table 1. The aural cue and spectrogram descriptions were
used for categorizing sounds into call types. The visual cue was used to help identify
the caller. The published descriptions include only those for Asian elephants.
21
Table I: Acoustic ethogram of putative call types showing single calls, trains of calls, and call combinations.
Call type and spectrogram
Basic Sounds
RUMBLE
ROAR "'
'""' ~ ~ 3000
~ Jt
2000
1000
00 05 10 1 5 20 lime ~
BARK "'
dOOO
6000
"""'
2000 - . ...
' c.~-·?l- - "-=-
0 1 O? 0.3 1} 4 05 06 time. s
Definition
Aurul cuf:' : Sounds like a diesel engine. motorboat. distant helicopter: PuJsatmg with a t1a1 pitch. Perceived as low frequency.
Spnrrogram inspt'Crion: Flar fundamental frequency with narrow bandwidth. Pulsating in spectrogram associated w/ amplitude modulation in waveform. Harmonics present.
Visuul me ufcaller: Mouth visibly open. forehead flutter.
Puhlished Jescriprion: A resonant growl (Olson. 2004). Growl modified by resonance in trunk. used in contex1 of mild arousal (McKay. l 97J). Call with fundamental frequency in range l 4 Hz to 30 Hz. lasting 10-1:5 sec.
Aural cuf:': Sounds like a lion roar with changing pitch Pitch sounds like it changes because of amplitude modulation. Perceived as low frequency.
Spt'crrogram inspecrion: Flat or slightly modulated fundamental frequency. Amplitude modulated. with greatest amplitude somewhere during the call rather than at beginning or end. Tonal. noisy. or mixed. Sometimes ends in rumble. Broader bandwidth than rumbles. but inconsistent. Harmonics present.
Visuul cue of caller: Mouth wide open or widens during call. lower abdomen contracts.
Puhlished description : Growl modified by amplitude increase. used in context of long-distance contact. Used primarily by juveniles who have been separated from their group (McKay. 1973 ). Pulsating sound. Loud growl (Olson. 2004 ).
Aural cuf:': Sounds like a short loud grunt. clearing throat. cape buffalo. chuff of cow. cheetah. Ends ahruptly with no roll off. Noisy compared to rumble.
Spt!crrogram inspn-rion: Flat hmdamentaJ frequency. Short duration. Ca!J is usuaJ!y noisy. Harmonics not always visible (or difficult to see with noise).
Visual cue of caller: Mouth open.
Puhlished description: None for A~ian elephants. but may be similar to the bark African elephant calves (Stoeger-Horwath et al .. 2007).
22
~ ~ 8000
i 4000
"""'
10000
aooo
0.IJ
Call type and spectrogram
aooo
6000
4000
2000
SQUEAK
0 0 0.2 o.3 o~
SQUEAK TRAIN
.,
SQUEAL
0 4 0.6 ume '>
o.e 1.0
Definition
Aural cue: Sounds like a wet finger across tight rubber surface. audible pedestrian crossing signal. baby alligator. gibbon caJI. High-pitched sound that falls in pitch, powerful initial sound.
Spectrogram inspection: Descending fundamental frequency that is either straight or curved in shape. Small shon blip may precede pitch fall. Harmonics present. Modified by repetition.
Visual cue of caller: Cheeks depressed. mouth open slightly or closed. tail sometimes erect.
Published description: Not heard in wild (McKay. l 973).
Aural cue: Train of repeated Squeaks with short breaks between Squeak sounds of about l sec. Rate of squeaking may change.
Spectrogram inspection: Squeaks are in a train if time between edges (end of one/beginning of next) of Squeaks is less than l sec. Ot was easier to perceive a break of l sec than 0.5 sec. so l sec was used to define a train versus a bout of individual calls.) Amplitude sometimes shows decrease of subsequent Squeaks _towards the end of the train. Harmonics present.
Visual cue of caller: Cheeks depressed, mouth open slight] y or closed. tail sometimes erect.
Published description: Chirp=multiple short squeaks. Possibly in context of conflict (McKay. l 973).
Sounds like rubber shoes on a gym floor, release of air out of a baJloon, windshield wiper on a dry window. a dog whine. Short utterance.
Acoustic description: Flat or modulated pitch. Short duration (less 1-J .5sec from aural cues), usually flat in pitch or difficult to tell (because it looks like just a point). Harmonics present. Modified by repetition.
Visual cue of caller: Cheeks depressed, mouth open slightly or closed.
Published description: none
23
0000
~
r~ 4000
2000
aooo
~ "'6000
J .... 2000
6000
~ "'6000
! """' 2000
Call type and spectrogram
SQUEAL TRAIN
IO
SQUEAL LONG
0 0 02 0 4 0 .6 08 I 0 .., '""' '
Definition
Aural cue: Train of repeated Squeal with shon breaks between sounds. Sounds like squeals in staccato. intennittent release of air from a balloon. Each squeal sounds flat in pitch but they jump up and down.
Specrrogram inspecrion: Squeals are in a train if time between edges (end of one/beginning of next ) of Squeals is less than l sec. Flat fundamental frequency of each squeal. with frequency shifts or jumps between Squeals in the train. Harmonics present.
Visual cue of cu.ller: Cheeks depressed. mouth open slightly or closed.
Published description: none
Aural cue: Sounds like continual release of air from a balloon. fingers on a wet baJloon. sliding pitch. sounds Jong.
Specrrogram inspection: Modulated fundamental frequency with many inflection points. Longer than Squeal but duration variable. clearly longer than l .5-2sec. Sound is continuous with no apparent breaks and pitch slides rather than jumps. Frequency values similar to Squeal Trains but with continuous energy rather than breaks. Harmonics present.
Visual cue of caller: Cheeks depressed. mouth open slightly or closed.
Published description: none
Aural cue: Sounds like a trumpet blast. Perceived as a higher frequency call.
Specrrogram inspection: Flat fundamental frequency. Narrowband. Sometimes has broadband energy at higher frequencies (top hat of noise). Harmonics present.
Visual cue of caller: maybe a lifting at base of trunk. trunk sometimes extended. tail sometimes erect sometimes (any behavior indicating extreme arousal).
Published description: Squeak modified by increased amplitude and duration of call. Used in context of extreme arousal. Pulsating sound (McKay. l 973).
Aural cue: Forced exhalation through end of trunk. Not a vocalization.
Specrrogram inspection: Noisy broadband. Looks like a foot scrape. but lower frequency than foot scrape. Harmonics not clearly visible.
Visual cue of caller: air blast from trunk
Published description: May be same as snort in McKay (1973).
24
Call type and spectrogram
BLOW-TONAL
250C
1000 rl
~ 1000 !!1
; I
lw'° ii
""' o:i os 10 15 20 2.5 30 35
'""'.
Combination calls
Definition
Aural cue: Sounds like blowing into a glass bottle or rubbing water on the rim of a crystal glass. Very clear.
Specrrogram in~pection: Tonal. Flat fundamental frequency. Harmonics not clearly visible.
Visual cue of caller: Forehead protrudes enough to see at a distance. Produced only by one animal in study (Oregon Zoo male. Tusko).
Published description: none
Calls in which constituent parts are basic calls types. but with a continuous frequency contour and no apparent break in sound between the constituent parts . The criterion of continuity differentiate combination calls produced by a single individual from overlapping calls produced by multiple individuals.
'"" 3500
:woo
~ ;.~
Ii ~ 2000
/! 1500
,,..
"'°
•0000
.,,., ~
rff.Ol'IO
~
1-"""'
'""" '""' ""'
·10
ROAR/BARK + RUMBLE
., ,, •• .. " "
A Roar or Bark ending in a Rumble.
Same acoustic structure as singles calls. but with continuous sound and frequency contour.
May just be a variance of Roar. Many Roars have at least a slight fall off at the end that sounds like a Rumble. In (Stoeger-Horwath er al.. 2007). Roars are broken down into mixed Roars of various combinations.
Any combination of Squeals and Squeaks occurring in a train with less than l sec between the edges of the calls.
Same acoustic structure as singles calls.
A Squeak ending with any variation of Bark. Rumble. or Roar. Usually ends with a Bark.
Same acoustic structure as singles calls. bur with continuous sound and frequency contour.
25
Call type and spectrogram
TRUMPET+ ROAR/RUMBLE
.
:;.:; >.
Definition
A Trumper endmg. in a Roar or Rumble. Trumpet ending. in Rumble' is rare .
Same acoustic structure as singles call s. bul with continuous wund and frequency contour.
26
CHAPTER III: CALL DISTRIBUTION AND CALL RATE - HOW THE
REPERTOIRE IS USED BY GROUPS AND INDIVIDUALS
METHODS
Comparing call distribution of groups and individuals
To compare incidence of call types among groups and individuals, the number
of calls of each putative call type (Table 1) was totaled for the study sites and for
individuals in the Oregon Zoo herd. For this comparison, the following calls were
removed from the dataset: duplicates of calls recorded with multiple systems, Blows,
and recordings of one female elephant who vocalized on command. All combination
calls with basic call types as the constituent parts were combined into one group called
"combo."
The call rate was not weighted to account for differences in recording time
among sessions. For comparison of the Oregon Zoo herd to domesticated elephants in
Thailand, the recording duration, sample size of calls, and number of subjects were
very different, but the contexts in both sites offered situations of mild to extreme
arousal. For comparing individuals within the Oregon Zoo herd, the call rate was not
weighted because the total recording time for the cows was similar. Also, the caller
has not yet been identified for every call, so these call compositions provide only a
snapshot of how each cow uses the repertoire. Only one male, Tusko, was included in
this comparison because of limited data for the other males.
27
Comparing call rate in various social contexts
The rate of calling, regardless of the caller, was compared among the various
contexts in which data were collected at the Oregon Zoo. For this comparison, the
following calls were removed from the dataset: duplicates of calls recorded with
multiple systems, Blows, recordings of one female elephant who vocalized on
command, and males recorded when by themselves. The total number of calls was
2147. The number of calls of each call type per recording session were calculated,
then adjusted to a 60 minute recording duration. The social contexts in which
recordings were made are shown in Table 2. Most of the recordings were scheduled
during social introductions, with some opportunistic recordings.
Table 2: Social contexts for call comparison
Context Description #of sessions
Routine No specific event, normal training, husbandry, shifting 6
Enrichment Novel enrichment in yard (not just browse, maybe new tires or 3 something rare)
Introduction 1 male in with female group (breeding, intro), includes howdy 39
Reunion Females join after separation of group for an Introduction event 1 after introduction
Separation Focal animal is separated and by themselves (not with male as in intro) 2
Reunion Females join after a Separation event 1 after separation
Transfer Arrival of bu11 Tusko 1
Death Euthanasia of matriarch Pet, including time during euthanasia, visitation 1 of herd members to the body, other herd members after leaving the body
28
RESULTS
Call distribution between sites
With the exception of the Squeak call. the domesticated elephants recorded in
Thailand produced the same repertoire as the Oregon Zoo elephants. but with a
different distribution of calls, as shown in Figure 1. The Squeak call was made by
Thai elephants. but only when asked to speak or when engaging tourists. Some factors
that potentially account for the difference in call distribution are a difference in social
context. difference in number of animals in the herd. environmental noise, and
individuality.
A.
Corrbo Rurrble
6%
25%
B.
37%
Combo
3%
Squeal
23%
Rumble
Roar 12%
ark
Figure J : Call distribution of the Oregon Zoo herd versus domesticated elephants in Thailand Panel A shows the call distribution of the Oregon Zoo herd (N=2066 calls). Panel B shows the call distribution of domesticated elephants in Thailand (N=279 calls) .
Call distribution among individuals
Individuals within the Oregon Zoo herd use the repertoire quite differently. as
shown in Figure 2 and Figure 3. For most of the recording sessions, the cows were
together as a group, so the difference in calls produced could be a function of
29
individuality. differences in roles within the herd. or differences in level of arousal
within the same social context. Investigating these differences is planned with future
analyses. Only one of the three bulls was recorded consistently during the course of
this study, and this bull produced primarily one sound. a tonal Blow.
A.
Trurrpe\
0%
c.
RuntJle Roar ContJo 1% 1%
Bark
0% Squeak
0%
B.
RuntJle ContJo 1% Roar Bark
3°/o . . .'.· ·. ~
Squeal 27%
D.
Figure 2: Call distribution of females in the Oregon Zoo herd.
Squeak 54%
Panel A shows Pet. Panel B shows Sung-Surin (Per's daughter). Panel C shows Rose Tu. Panel D shows Chendra. Panel E shows Tusko (male).
30
Run"ble 0% Roar
6% Squeak
0% Squeal
0%
2% Con"bo
Blow-tonal
81 %
0%
Figure 3: Call distribution of one male (Tusko) in the Oregon Zoo herd.
Call rate in various social contexts
Figure 4 shows a comparison of call rates as a function of social context. The
highest rate was in temporary separatjons and reunion after those separatjons. Not
surprising is the low rate of calling during routine husbandry. What is surprising is the
relatively low rate of calling during the death of the matriarch, the transfer in of a bull.
and social introductions. Given that elephants use many modes of communication, it is
possible that the rate of acoustic production for this herd is not a reliable indicator of
arousal, or that what we perceive as a situation that warrants increased arousal does
not actually do so.
31
70
60
.. 50 :::J 0 ;i: GI 40 'tii a:
~ 30 c Ill
~ 20
10
0
-~0 ~ «-0
~ ~0
!...°"'"" i..'O'
«,.~
Call Rate by Context
.,p ~
~-s
-o~
J>-'>CJ
'~ ·t:l -o~'b' ~
«-0"
~o ,~
~o~ . ~'lf c-;,0~
<!::-0' 'lf
~<§:' 'b-{b'
c-;,0~
-o~ ~ «-0-s
Context
Figure 4: Mean call rate as a function of social context
~~ ~<:<)
"{b'
!§' ()0
32
CHAPTER IV: ACOUSTIC PARAMETERS AND AUTOMATIC
CLASSIFICATION OF CALLS
METHODS
Measuring acoustic parameters
Praat scripts were run to make a separate sound file for each vocalizations.
Only sounds of basic call types (Bark, Roar, Rumble, Squeak, Squeals, Trumpet) and
Blows with no overlapping sporadic sounds were selected for measurement. All calls
were measured at the original sample rate of either 32 kHz or 44 kHz.
Osprey (vl.7) on MATLAB (v7.7, Mathworks, Inc.) was used to measure the
acoustic parameters in order to characterize the signal structure of each call type.
Osprey's measurement system was developed for characterizing the marine sounds in
the Macaulay Library of Natural Sounds. Osprey measurements are based primarily on
Fristrup and Watkin's (1993) AcouStat approach, with additional measurements and
modifications that use estimators of central tendency and dispersion that are robust to
outliers, namely the median, interquartile range, and quartile skewness. Measurements
that use these estimators of central tendency have more consistent values at variable
noise levels than measurements that rely on manual selection of signal extremes
(Mellinger & Bradbury, 2007; Cortopassi, 2006). Signal extremes are sensitive to
noise and outliers. In addition, manually measuring signal extremes requires
assessment of signal onset and offset in both time and frequency, which can be
affected by display settings and can be biased by researcher expectation and
experience (Cortopassi, 2006).
33
Calls of the same type and sampling rate were merged together into a sequence
of calls for quicker and more consistent measuring. Spacers were added between calls
in the merged file for Rumbles and Squeals to help distinguish separate call s. The
spectrogram parameters in Osprey were set to show separation of harmonics and good
detail in both time and frequency. and were set the same for each call type (window
type=Hamming, hop size=14 , zero padding=lx. frame size (digital samples per FFf)
=512 to 2048, for a filter bandwidth of 63 to 349 Hz). An annotarion box (or selection
box) containing the entire sound in a region of time and frequency was drawn liberally
to contain the entire sound plus some background noise, as shown in the two example
calls in Figure 5 and Figure 6. Because only calls with no overlap of other sporadic
sounds were used for measuring acoustic parameters. the annotation box included only
the ambient noise and the focal sound.
8000
.. ::i:. 6000 >. 0 c .. :::J er .. .:: 4000
2000
0 000 0.05 0 10 015 020 025 030 0 35 0 40 0 45 050
time . s
Figure 5: Osprey window of Squeak with annotation box and feature box. X-axis is time. Y axis is frequency. Coloration is intensity. Annotation box encompasses the entire signal. Feature box encompasses 90% of the signal energy.
34
12000
10000
:r 8000
~ c::
" " ~ 6000
4000
2000
0.2 0.4 0.6 0.8 1.0 1.2 1 4 1 8 20 time. s
Figure 6: Osprey window of Trumpet with annotation box and feature box. X-axis is time. Y axis is frequency. Coloration is intensity. Annotation box encompasses the entire signal. Feature box encompasses 90% of the signal energy.
Osprey creates a spectrogram of the annotation box and applies a de-noising
algorithm to the entire spectrogram to remove general ambient noise (Mellinger &
Bradbury. 2007). It then creates a feature box that encompasses the inner 90% of the
signal energy in time and frequency, with the strongest 90% of the signal represented
and the weakest 10% excluded. In the time domain (horizontal axis in the
spectrogram), energy in each column is summed, with the sequence of sums forming a
row vector known as the time envelope. The row vector is then ranked (sorted) highest
to Lowest in energy. and the cumulative sum beginning at the high end is computed
until 90% of the total energy is reached. The earliest and latest time indices included
in this 90% cumulative sum define the time bounds of the feature box. The inner 90%
is the upper 90% after the values in the time envelope are sorted into ascending order.
An analogous process happens in the frequency domain (vertical axis in the
35
spectrogram), with the sequence of row sums forming the frequency envelope (power
spectrum), and the 90th percentile of the sorted energies being used to define the
frequency bounds of the feature box.
After the feature box is established, Osprey extracts 28 acoustic features within
this feature box and calculates the signal to noise ratio within the annotation box.
Osprey weights measurements at each instant by the normalized intensity of the signal
at that instant. This weighting means that louder parts, which are least affected by
background noise, have the strongest influence on the measurement value (Mellinger
& Bradbury, 2007).
For calls that have low frequency energy around 20 Hz (Rumbles, Barks, and
Roars), the frequency of the fundamental was verified by measuring the harmonic
spacing of the call in Praat. The annotation box was then drawn so the boundary
matched the fundamental frequency rather than drawing it more liberally to include
energy below the fundamental frequency. The reason for this was twofold: (1) one
goal of this study was to determine the frequency range of elephant calls, so an
accurate measure of the minimum frequency for low frequency calls was needed, and
(2) drawing the annotation box below the fundamental frequency sometimes resulted
in the inner 90% of the signal energy encompassing energy below the frequency that
was verified as the fundamental frequency by harmonic spacing.
36
Acoustic parameter definitions
The acoustic parameters measured by Osprey are illustrated in Figure 7 and
described in Table 5. A complete descrjption of the calculation of these parameters is
given by Mellinger & Bradbury (2007).
A.
10000
8000
... ~ 6000 :>. u i :;J
e-- 4000
2000
M11JM20
0
B. M17 M7 time. s
i~)
1· 1 . M25
/
v-Ji\ · 1~ ID \ V\ M27 l. I) I •
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M20 ' ''°lt.ielr(\ ti :
Figure 7: Acoustic parameters measured by Osprey that can be visualized Panel A shows the spectrogram. Panel B shows the power spectrum.
37
Table3: A dbvO
Parameter Units Description Type
Ml-M6: Feature box
Start Time sec Lowest time index in bounds that encompass the temporal (Ml) inner 90% of the signal strength in the time
envelope.
End Time sec Highest time index in bounds that encompass the temporal (M2) inner 90% of the signal strength in the time
envelope.
Lower Frequency Hz Lowest frequency index in bounds that encompass frequency (M3) the inner 90% of the signal strength in the frequency
envelope.
Upper Frequency Hz Highest frequency index in bounds that encompass frequency (M4) the inner 90% of the signal strength in the frequency
envelope.
Duration sec Width of feature box: M2-Ml temporal (M5)
Bandwidth Hz Height of feature box: M4-M3 frequency (M6)
M7-M14: Central values and variation
Uses measures that do not assume normality: median, quartile ranges, quartile skewness, concentration.
Median Time sec Time at which 50% cumulative signal energy is temporal (M7) reached.
(Measured relative to start of file, so M7 was calculated as M7new=M7-Ml)
Temporal Interquartile sec Concentration of a call around the median time (M7) temporal Range measured as the duration of the interquartile range of (M8) signal energy (Q3-Ql).
Counts energy going forward and back from the median time (M7). Q3=median + 25% of signal energy Q l=median-25% of signal energy
Temporal sec Concentration of a call measured as the time span temporal Concentration encompassing loudest 50% of time envelope values. (M9) Counts energy from the loudest parts down towards
the smallest parts regardless of where the parts occur in time.
Temporal Asymmetry none Skewness of energy along time axis within temporal (MlO) interquartile range (-1.0 to 1.0)
38
Parameter Units Description Type
Median Frequency Hz Frequency at with 50% cumulative signal energy is frequency (Mll) reached.
More stable than extreme values of LowerFreq and UpperFreq in varying noise conditions.
Spectral Interquartile Hz Concentration of a call around the median frequency frequency Range (M 11) measured as frequency range of interquartile (M12) range of signal energy (Q3-Q 1 ).
Counts energy going forward and back from the median frequency (Ml I). Q3=median + 25% of signal energy Ql=median-25% of signal energy
Spectral Concentration Hz Concentration of a call measured as the frequency frequency (M13) span encompassing loudest 50% of frequency
envelope values. Counts energy from the loudest parts down towards the smallest parts regardless of where the parts occur in time.
Frequency Asymmetry none Skewness of energy along frequency axis within frequency (Ml4) interquartile range (-1.0 to 1.0)
M15-M20: Peak intensity
Time of Peak Cell sec Time of single loudest spectrogram cell. temporal Intensity Time of the cell containing the peak intensity. (Ml5) (Measured relative to start of file, so MIS was
calculated as M15new=M15-Ml)
Relative Time of Peak % Relative time of peak intensity (M15/M5) temporal Cell Intensity (M16)
Time of Peak Overall sec Largest value in time envelope, which is the largest temporal Intensity vertical sum of the spectrogram over all frequencies. (M17) Time of the peak intensity in the trimmed time
envelope. (Measured relative to start of file, so M7 was calculated as Ml 7new=Ml 7-Ml)
Relative Time of Peak % Relative time of peak intensity (M 17 /M5) temporal Overall Intensity (M18)
Frequency of Peak Hz Frequency of cell containing the peak intensity. frequency Cell Intensity (M19)
Frequency of Peak Hz Frequency of peak intensity in the trimmed frequency Overall Intensity frequency envelope. (M20)
39
Parameter Units Description Type
M21-M24: Amplitude and frequency modulation (variation of amplitude and frequency over time)
AM Rate Hz Dominant rate of amplitude modulation. amplitude (M21) Frequency of the maximum rate in the power
spectrum of the trimmed time envelope.
AM Rate Variation Hz Variability of amplitude modulation measured as the amplitude (M22) width of peak at M21-6 dB.
Values are discretized because at 6dB down from the peak, the widths may be a only a few bins wide so the values are integer multiples of the bin width.
FM Rate . Hz Dominant rate of frequency modulation. frequency (M23) Frequency of the maximum rate in the power
spectrum of the trimmed frequency envelope.
FM Rate Variation Hz Variability of frequency modulation measured as the frequency (M24) width of peak at M23-6 dB.
(How much the rate of change varies, may be related to inflections and steepness of upsweeps and downsweeps)
M25-M28: Fine features of harmonic structure, shifts in periodicity, direction of frequency change, rate of change in frequency
Cepstrum Peak Width Hz Harmonic structure structure (M25) Average width of peaks (harmonics) in power
spectrum. Peak width is measured at 6 dB down from maximum value. At 6 dB down from the peak, the widths may be a only a few bins wide (like M22 and M24), but M25 is an average of integers so the values are not discretized. Narrow peaks means narrowband/tonal harmonics.
Overall Entropy Hz Entropy, shifts in periodicity structure (M26) Distribution of energy across frequency blocks in a
given time block. Shift from periodicity and linearity to chaos. Change in noisiness v. tonality.
Upsweep Mean Hz Direction of frequency change frequency (M27) Measures how much the frequency increases.
Average change in median frequency between successive time blocks, weighted by total energy in the block. Inflection points with rising and falling frequencies throughout call result in a low M28 (closer to 0) compared to a consistent directional change. Measure is weighted to emphasize contribution oflouder signal components. M27<0 means frequency is decreasing.
40
Parameter Units Description Type
Upsweep Fraction % Rate of directional frequency change frequency (M28) Counts the number of times the frequency content
increases. Fraction of time in which the median frequency in one block is greater than in the preceding block, weighted by total energy in the block. Indicates how much of the call has a directional change in the frequency. Inflection points with rising and falling frequencies throughout call result in a high M28, just as a consistent directional change. Measure is weighted to emphasize contribution of louder signal components. M28 always positive.
M29: Signal strength
Signal-to-Noise Ratio dB Signal to noise ratio within the annotation box. amplitude (M29) Ratio of the signal power (loudest cell) to the noise
power (power of cell at 25th percentile). Cells are ranked low to high and the cell at the 25th percentile represents noise. (25th percentile is used because the animal call likely takes up less than 75% of the total spectrogram cells.) Measurement assumes that the within the annotation box at least 25% of the cells are without a focal signal.
Dataset for measuring acoustic parameters
The original dataset contained 2791 calls representing all call types, including
combination calls. Acoustic parameters were measured for basic call types (Bark,
Roar, Rumble, Squeak, Squeals, Trumpet) and Blows having no overlapping sporadic
sounds and no echo (N=lOl 1). Calls that met the following criteria were removed
from this dataset for statistical analysis: duplicate calls using different recording
systems (N=83) and calls with a low signal-to-noise ratio (SNR< 10 dB) (N=l). The
final dataset used for statistical analysis is shown in Table 4. It is important to note
that the number of samples per call type was not balanced, so the statistical power is
41
low. Also, Blow sounds were not scored for every occurrence, so the sample size was
very low relative to the number of occurrence.
Table 4: Calls in dataset for statistical analysis (N=927)
Call type Oregon Zoo (N) Thailand (N) Total (N)
Blow 73 5 78
Bark 55 8 63
Roar 98 10 108
Rumble 38 21 59
Squeak 193 5 198
Squeal 211 31 242 (includes Squeal, Squeal train, and Squeal long,)
Trumpet 123 56 179
TOTAL 791 136 927
Process for statistical analysis for call classification
The goal of the statistical analysis was to determine if the data support
automatic classification of calls that were previously categorized using perceptual
aural cues and visual inspection of spectrograms, and to determine which acoustic
parameters differentiate the call types. The process for the analysis was as follows:
1) screen data to investigate distributions and variability, 2) standardize data to
account for different units of measure, 3) determine parameters to use for statistical
analysis, 4) remove outliers, 5) determine parametric or non-parametric statistical
analyses, and 6) run statistical analysis for automatic classification. All statistics were
run using R (v2.8.1).
Screening data for statistical analysis
Histograms and boxplots were created to determine the distribution and
variability among call types, to do a preliminary assessment of variables that
42
differentiate call types, and to learn more about how these variables reflect perceptual
aural cues.
Standardizing the data for statistical analysis
The acoustic parameters are measured in different units, so the data must be
standardized, or scaled, for the variables to be dimensionally homogeneous. A
boxplot of all variables was created for the scaled dataset, as shown in Figure 8.
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Figure 8: Boxplot of scaled variables across all call types (blow, bark, roar, rumble, squeak, squeal, trumpet) Acoustic parameters are on the x-axis. Scaled (normalized) values of the parameters are on the y-axis.
43
Removing parameters from statistical analysis
Some parameters were removed from the statistical analysis because they were
highly correlated with other parameters for this dataset (6 parameters) or were
absolute measures that could be represented by relative measures (4 parameters).
A correlation matrix was created using the scaled dataset of the basic call types
(Bark, Roar, Rumble, Squeak, Squeal, Trumpet) and Blow to determine potential
candidates for removal from further analyses. The correlation matrix measures the
strength and direction of the linear relationship between pair wise combinations of
variables (given by the Pearson correlation coefficient). Pairs of acoustic parameters
having high correlation coefficients were considered candidates for removal. Because
a correlation measures only the strength of linear relationships, it is possible that
additional pair wise combinations had a strong relationship, but the relationship was
not linear.
Correlations were greater than 0.5 for 14 pairs of variables. Although high
correlations could indicate redundancy and therefore suggest candidates for removal
from further analyses, not all candidates were removed because some relationships
may be biologically relevant for investigating acoustic communication of this species
as compared to others. For example, Overall Entropy (M26) was highly positively
correlated with three variables related to frequency range, Upper Frequency (M4 ),
Bandwidth (M6), and Median Frequency (M 11 ), so M26 was a candidate for removal.
However, the relationship of entropy to frequency range may provide insight into
sound production and perception. Frequency of Peak Overall Intensity (M20) was
highly correlated with Low Frequency (M3 ), so M20 was a candidate for removal.
44
However, this relationship confirms that the peak intensity occurs at or close to the
fundamental frequency of the call. Other high correlations were related to central
values and variation and their relationship to frequency and duration. These
correlations may indicate greater stereotypy in elephant calls compared to calls of
other species, so the relationship between these variables may be of biological interest.
Further analysis may be needed to determine measurements that are most important
given the study subjects and recording environment.
A total of 18 acoustic parameters were used in the statistical analyses, which
are shown in Table 5 with notation for each parameter that appears in the data output.
-Parameter Notation in Output Description
Lower Frequency (Hz) LowerFreqM3 Lowest frequency index in bounds that (M3) encompass the inner 90% of the signal strength
in the frequency envelope.
Upper Frequency (Hz) UpperFreqM4 Highest frequency index in bounds that (M4) encompass the inner 90% of the signal strength
in the frequency envelope.
Duration (sec) DurationM5 Width of feature box: M2-Ml (MS)
-
Bandwidth (Hz) BandwidthM6 Height of feature box: M4-M3 (M6)
Median Time (sec) MedianTimeM7 Time at which 50% cumulative signal energy is (M7) reached.
(Measured relative to start of file, so M7 was calculated as M7new=M7-Ml)
Temporal Concentration TimeConcentM9 Concentration of a call is measured as the time (sec) span encompassing loudest 50% of time (M9) envelope values.
Counts energy from the loudest parts down towards the smallest parts regardless of where the parts occur in time.
Temporal Asymmetry TimeAsymmM 10 Skewness of energy along time axis within (MIO) interquartile range (-1.0 to 1.0)
45
Parameter Notation in Output Description
Median Frequency (Hz) MedianFreqMl 1 Frequency at with 50% cumulative signal (M11) energy is reached.
More stable than extreme values of LowerFreq and UpperFreq in varying noise conditions.
Frequency Asymmetry FreqAsymmMl 4 Skewness of energy along frequency axis within (M14) interquartile range (-1.0 to 1.0)
Relative Time of Peak PkOverallRelTM 18 Relative time of peak intensity (Ml 7 /MS) Overall Intensity (%) (M18)
Frequency of Peak Pk0verallFM20 Frequency of peak intensity in the trimmed Overall Intensity (Hz) frequency envelope. (M20)
AM Rate (Hz) AMRateM21 Dominant rate of amplitude modulation. (M21) Frequency of the maximum rate in the power
spectrum of the trimmed time envelope.
AM Rate Variation (Hz) AMRate V arM22 Variability of amplitude modulation measured (M22) as the width of peak at M21-6 dB.
Values are discretized because at 6dB down from the peak, the widths may be a only a few bins wide so the values are integer multiples of the bin width.
FM Rate (Hz) FMRateM23 Dominant rate of frequency modulation. (M23) Frequency of the maximum rate in the power
spectrum of the trimmed frequency envelope.
FM Rate Variation (Hz) FMRate V arM24 Variability of frequency modulation measured (M24) as the width of peak at M23-6 dB.
(How much the rate of change varies, may be related to inflections and steepness of upsweeps and downsweeps)
Overall Entropy (Hz) EntropyM26 Entropy, shifts in periodicity (M26) Distribution of energy across frequency blocks
in a given time block. Shift from periodicity and linearity to chaos. Change in noisiness v. tonality.
Upsweep Mean (Hz) UpswpMeanM27 Direction of frequency change (M27) Measures how much the frequency increases.
Average change in median frequency between successive time blocks, weighted by total energy in the block. Inflection points with rising and falling frequencies throughout call result in a low M28 (closer to 0) compared to a consistent directional change. Measure is weighted to emphasize contribution of louder signal components. M27<0 means frequency is decreasing.
46
Parameter Notation in Output Description
Upsweep Fraction (Hz) UpswpFracM28 Rate of directional frequency change (M28) Counts the number of times the frequency
content increases. Fraction of time in which the median frequency in one block is greater than in the preceding block, weighted by total energy in the block. Indicates how much of the call has a directional change in the frequency. Inflection points with rising and falling frequencies throughout call result in a high M28, just as a consistent directional change. Measure is weighted to emphasize contribution of louder signal components. M28 always positive.
Removing outliers
Boxplots of all variables in the scaled dataset were created for each call type to
determine potential outliers within each call type. The criteria for determining outliers
was very conservative in order to preserve data that may have biological relevance.
Given that the gradients of variability may have biological relevance, data points were
considered outliers only if they deviated from the overall pattern of distribution and
did not appear to belong to a long tail (gradient), as shown in Figure 9. Calls were
considered outliers only if they were multivaried outliers. Because variance across two
variables could be a result of covariance alone, calls were considered outliers and
removed from the dataset only if they were outliers across at least three variables.
Because calls recorded in Thailand could represent variability in call structure not
found in the Oregon Zoo call repertoire, and the Oregon Zoo data is better represented
in the dataset, outlier Thailand calls were removed only if they sounded like they did
not meet the definition of the calls as established by aural cues.
47
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Figure 9: Scaled variables for Roar showing outliers and gradients Small circles in the plot indicate points that are outside of the interquartile range. Ellipses show examples of data points considered as outliers and data points not considered outliers.
Out of 927 calls, only 19 calls (Blow N=6, Bark N=4, Roar N=3, Rumble N=2,
Squeak N=l, Squeal N=3, Trumpet N=O) met the conservative criteria for outliers.
Table 6 shows the final dataset with outliers removed (N=908).
48
Table 6: Calls in dataset (N=908) after outliers removed
Call type Oregon Zoo (N) Thailand (N) Total (N)
Blow 68 4 72
Bark , 51 8 59
Roar 97 8 105
Rumble 36 21 57
Squeak 193 4 198
Squeal 209 30 239 (includes Squeal, Squeal train, and Squeal long,)
Trumpet 123 56 179
TOTAL 777 131 908
Determining parametric or non-parametric statistical analysis
After scaling and removal of outliers, normal probability plots were created for
the scaled dataset (N=908) to determine how well the normal distribution describes the
data. The normal probability plot is a quantile-quantile (Q-Q) plot against the standard
normal distribution. The observed data is ranked and the quantile is calculated. If the
observed data set matches the theoretical distribution, the shape of the plot will be a
straight line where y = x.
Based on inspection of the Q-Q plots, approximately half of the variables were
normally distributed and only slightly skewed, and half were either very skewed or not
normal. Therefore, this dataset does not meet the assumptions of normality, linearity,
and constant variance. By not meeting these assumptions, only non-parametric
statistics can be used.
49
Running statistical analyses for automatic classification
A classification tree was run with the original data (non-standardized) as a
preliminary analysis for determining the most important predictors for classification
into the basic call types (Bark, Roar, Rumble, Squeak, Squeals, Trumpet), and to
explore its potential as a predictive model for classifying new calls into the pre
defined call types.
A Principal Component Analysis (PCA) of the scaled dataset was run to
determine the parameters that explain most of the variance among call types. The main
objective of PCA is to reduce the dimensionality of the dataset. Ideally, PCA should
be multinormal and reasonably unskewed. Although the data did not meet these
assumptions, the clustering of data along principal component axes can still provide
insight into the variables that group and separate call types.
Global and pair-wise Analysis of Similarity (ANOS IM) tests of the scaled
dataset were run to determine if there were significant differences in the acoustic
parameters among the pre-defined call types and between pairs of call types. If two
call types are significantly different in their parameter values, then the dissimilarities
between the call types will be greater than those within each call type. In order to
evaluate the dissimilarity within and between call types as a measure of true distance
between parameter values, the Ward's linkage method was used with the Euclidian
distance. The number of iterations for assessing significance was 1000. A Bonferroni
correction was calculated to reduce the family-wise Type I error from 15% to 4.8%.
Other analyses considered were a Discriminant Function Analysis (DFA) and
unsupervised clustering. DFA is better than PCA for discriminating existing groups
50
and classifying into pre-defined categories; however, even the scaled data did not meet
the assumption of multinormality required by DF A. A hierarchical agglomerative
cluster analysis of the scaled dataset was run on the Oregon Zoo data using R.
Although there appeared to be at least a weak group structure in support of the pre
defined call types, the output plot was illegible given the number of data points so it
was not possible to determine other potential call groupings from inspection.
Unsupervised clustering should be considered for future comparison of call
classification methods.
The Blow sound was included in the distributions, but not the classification
tree, PCA, or ANOSIM tests. This non-vocal sound is made frequent~y by both males
and females and in most situations, so characterizing this sound by its acoustic
structure may be useful for acoustic monitoring. However, preliminary runs of the
classification tree and PCA showed that it complicated the classification and grouping
of the vocal sounds, so it was handled separately from the basic call types.
Comparing low frequency calls to background noise
Although low frequency vocalizations have the potential for communication in
forested environments and over long distances, they serve a communicative function
only if they can be detected by a receiver. Power spectra of low-frequency Rumble
calls and background noise were compared to investigate bandwidth and intensity of
Rumbles in relation to spectral characteristics and intensity of the background noise.
Only data collected with equipment capable of recording below 20 Hz were
used. Paired Rumble and noise samples were selected from the same recording to
51
ensure that the recording levels were equal. Noise segments were selected as close as
possible to the Rumble to take into account that a caller may adjust the intensity of
sound production depending on background noise. The noise segments were selected
with a duration approximately equal to the rumble. A frequency range of 14 Hz to
1.2 kHz was used to compare bandpass characteristics because rumbles have a range
of 14 Hz to 1.2 kHz for the inner 90% of the call energy. Thailand recordings were
separated by site because one site is rural (Elephant Nature Park) and the other is
urban (Royal Elephant Kraal).
Osprey uses an arbitrary reference to generate power spectra, where a sample
value of 1 is effectively 0 dB. Hence, comparisons of signal and noise can be made
only if the recording levels are equal so that the power is referenced to the same value.
With this reference to the same value, the signal to noise ratio at any frequency value
is the difference of the signal power (dB) and noise power (dB). To make these
comparison, the signal and noise were not separated within each sound segment;
rather, the spectrums for the paired Rumble and noise segments were compared
visually to estimate the difference between the signal power and the noise power at
various frequency values.
The sample size used in these analysis was small (Oregon Zoo N=3, Elephant
Nature Park N=4, Royal Elephant Kraal N=3), so this does not constitute a complete
noise analysis, but it does offer some insight into the potential of Rumble calls to be
buried in noise in various recording environments. Future analyses include a more
52
quantitative analysis of the signal to noise ratio at various frequency values for an
increased number of call and noise samples.
RESULTS
Call types
Six basic vocalization types were identified as structurally different, and were
labeled Bark, Roar, Rumble, Squeak, Squeal, Trumpet. An additional non-vocal
sound, Blow, was also found.
Frequency range, bandwidth, and duration of acoustic repertoire
The frequency range of the basic call types (Bark, Roar, Rumble, Squeak,
Squeal, Trumpet) and Blow encompassing all of the signal energy was 14 Hz to
18 kHz. The frequency range encompassing only 90% of the energy was 14 Hz to
9 kHz. The bandwidth encompassing 90% of the signal energy for individual calls
ranged from 54 Hz to 9 kHz (median 1680 Hz). The duration encompassing 90% of
the signal energy varied from 0.1 sec to 14 seconds (median 0.7 sec).
Variability of acoustic parameters among call types
Only a subset of the graphical results are included here. All plots are provided
in the appendices. By comparing the histograms and boxplots, it appears that the
distribution of each variable differs among call types, as shown in Figure 10. From the
distributions of commonly used measurements (e.g., fundamental frequency and
duration), one can see how the acoustic parameters reflect aural perception. For
example, the fundamental frequency, as measured by Lower Frequency (M3), is
53
consistent or stereotypic for Bark (BRK), Roar (ROR), Rumble (RUM) and Squeak
(SQK), but varies more widely for Trumpet (TMP) and even more so for Squeal
(SQL). Blow shows the greatest variance, but this may be because it is a non-vocal
sound that is mostly broadband noise. Consistent with perception by aural cues, Bark,
Roar, and Rumble are lower in frequency than Squeak, Squeal, and Trumpet. Most of
the energy in Rumbles is low, whereas Roars have energy that extends higher. The
Duration (M5) is stereotypic for Trumpet, Squeak, Blow, and Bark, but his highly
variable for Rumble and Squeal. Squeal include the longest calls, followed by Rumble
then Roar.
54
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Figure 10: Example histograms and boxplots of acoustic parameters for each call type show distribution and variability. TMP=Trumpet, RUM=Rumble, SQK=Squeak, SQL=Squeal, BLW=Blow, BRK=Bark, ROR=Roar. Panel A and B show the Low Frequency (M3) parameter values or each call type. Panel C and D show the Duration (M5) parameter values for each call type.
55
Classification tree
A classification tree was run on the non-standardized data (N=836), with
outliers removed and excluding Blows. The tree served as a preliminary analysis for
. determining the most important predictors for classification, and may have potential as
a predictive model for classifying new calls into the pre-defined call types (Bark,
Roar, Rumble, Squeak, Squeal, Trumpet). The final classification tree model is shown
in Figure 10. Tree pruning was used as cross-validation for determining the final
model. The goal of cross-validation was to evaluate alternative models that reduce the
number of branches, but when the number of branches was reduced , two call types
(Bark and Rumble) were left out and the misclassification rate doubled. The final
model explained 78.4% of the variation among call types using decision rules with
only six variables: Lower Frequency (M3), Upper Frequency (M4), Duration (M5),
Frequency of Peak Overall Intensity (M20), FM Rate Variation (M24), and Upsweep
Fraction (M28).
The overall misclassification rate was 12.2%, which means successful
classification of 87 .8% of the calls into the predicted call type. Table 7 and Table 8
show the confusion matrix and the classification rate for each call type. The Squeak
had the highest successful classification rate (95 .9% ), followed by Squeal (91.2% ),
Trumpet (91.1 % ), Rumble (78.9% ), Roar (73.3% ), then Bark (71.1 % ). Rumbles were
most often misclassified as Roars. Roars were most often misclassified as Rumbles.
Barks were most often misclassified as Trumpets. The sample sizes of Bark and
56
Rumble were low, which may impact the decision rules and thus successful
classification of these call types.
The tree was most successful in classifying the group of higher frequency calls
(Squeak, Squeal, and Trumpet), but classifying Squeaks and Squeals required multiple
decision rules. Squeals were best differentiated by having a Lower Frequency (M3)
above 675 Hz, but for those Squeal calls that did not meet this criteria, it appears that
Upper Frequency (M4), Duration (M5), and FM Rate Variation (M24) differentiated
this call type. The Squeal call type is highly variable, and it appears from the decision
rules that misclassifications are primarily with the short Squeal call, which are often
low intensity compared to Squeal trains and long Squeals. Squeaks were best
differentiated by a low value for FM Rate Variation (M24), which means the
frequency modulation rate was relatively consistent throughout the call. For those
Squeaks that did not meet this criteria, it appears that Upper Frequency (M4),
Upsweep Fraction (M28); Duration, and Lower Frequency (M3) differentiated this call
type.
The group of lower frequency calls (Bark, Roar, and Rumble) appears to be
primarily differentiated by low values of Lower Frequency (M3) and Upper Frequency
(M4), then by Duration (M5). Among these call types, the Bark is separated from the
Roar and Rumble by its short duration. The Roar and Rumble are separated by the
Frequency of Peak Overall Intensity (M20), with the Rumble being below 170 Hz.
Given that Roars and Rumbles were misclassified most often as each other, this single
parameter may not be sufficient for separating these call types.
57
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Table 7: Confusion matrix for tree Diagonal cells (in grey) show correct classification into the predicted class. Off-diagonal cells show misclassifications. Columns represent the distribution of calls into each leaf (call type). Rows --r------- --- ------------ -- ------ ---- - .-- ,----------- -- - -- - . .,.. __ ,,_
Bark Roar Rumble Squeak Squeal Trumpet
Bark 42 11 0 0 1 1 Bark class contains 42 barks, 11 roars, 1 squeal, 1 trumpet
Roar 0 77 11 0 2 2 Rumble 4 13 45 0 0 0 Squeak 1 0 0 189 5 3 Squeal 0 0 0 3 218 10 Trumpet 12 4 1 5 13 163
I•~ 59 105 57 197 239 179 # calls of each call type
59 bark calls recognized by tree as bark (42), rumble (4), squeak (1) trumpet (12)
Table 8: Classification rate ( % ) for tree Diagonal cells (in grey) show correct classification into the predicted class. Off-diagonal cells show misclassifications
Bark Roar Rumble Squeak Squeal Trumpet Bark 71.1 10.5 0 0 0.4 0.6 Roar 0 73.3 19.3 0 0.8 1.2 Rumble 6.7 12.4 78.9 0 0 0 Squeak 1.7 0 0 95.9 2.1 1.7 Squeal 0 0 0 1.3 91.2 5.6 Trumpet 20.3 3.8 1.8 2.1 5.4 91.1
t Bark calls classified correctly at 71.1 % with a 28.9% misclassification rate
Parameters measured by Osprey are numerous, and one could limit the
parameters used in the model to those commonly measured by spectrographic analysis
tools to design a tree that could be used widely by researchers of Asian elephant calls.
Principal Component Analysis
A Principal Component Analysis (PCA) of the scaled dataset (N=836) with
outliers removed and excluding Blows was run to determine the parameters that
account for the variance within and among call types. In accordance with broken.stick
validation, which determines whether the observed pattern is significantly different
from a random pattern, only PCl and PC2 should be kept. PCl and PC2 explained
59
only 42.8% of the variance, with PCl explaining 24.6% of the variance and PC2
explaining 18.2%.
The PCA plot in Figure 12 shows some support for the grouping of call types,
but with mixing of call types. The relative contribution of each variable to the
principle components, or loading of principal components, highlights a subset of the
variables as being important for separating call types and grouping calls within each
type. The sign of the numerical loading value indicates whether the value of that
parameters increases or decreases along the principal component axes. The loading is
expressed in the eigenvectors of PC 1 and PC2 below:
PCl= (-0.29)LowerFreqM3 + (-0.42)UpperFreqM4 + (-0.38)BandwidthM6 + (-0.44)MedianFreqM11 + (-0.38)Pk0veral1FM20 + (0.23)AMRateVarM22 + (0.21)FMRateVarM24 + (-0.38)EntropyM26
PC2= (-0.14)LowerFreqM3 + (-0.52)DurationM5 + (-0.51)MedianTimeM7 + (-0.51)TimeConcentM9 + (-0.10)Pk0verallFM20 + (0.24)AMRateM21 + (0.24)FMRateM23 + (-0.19)UpswpFracM28
60
-----i i
.. !)
0 I ~
' f"~:r~· ~' '..('
,,.-~· ~ -
~-~---r
~ .. c
"" ~· ~ . "' c . Q_
<> BRK -+ . ROR 0 RUM
SOK 0 _J I SOL
TMP
i.J.- j
-5 0
oc 1
Figure 12: Principal Component Analysis plot for basic call types
(~
Oo 0 c
0 ( •
~;'
:::j
0 c•
0
TMP=Trumpet. RVM=Rumble. SQK=Squeak. SQL=Squeal. BL W=Blow. BRK=Bark. ROR=Roar PC I is represented primarily by spectral parameters. so clustering along PC l results primarily from variability in spectral structure. PC2 is represented primarily by temporal parametc-rs. so clustering along PC I results primarily from variability in temporal patterns.
Along PC l axis. the variability is explained by AM Rate Variation ( M22 ).
Overall Entropy (M26 ). and frequency parameters. namely Lower Frequency (M3 ).
Upper Frequency (M4). Bandwidth (M6). Median Frequency (Ml J ). Frequency of
Peak Overall Intensity (M20J. and FM Rate Variation (M24). The loading of AM Rate
V aria ti on (M22) and FM Rate V aria ti on (M24) is positive. so the value of these
61
parameters increase along the PCl axis. All other variables decrease along the PCl
axis. Starting at the most positive end of the PCl axis, Rumbles have the lowest values
for Lower Frequency (M3), Upper Frequency (M4), Bandwidth (M6), Median
Frequency (Mll), Frequency of Peak Overall Intensity (M20), and Overall Entropy
(M26), and the highest values for FM Rate Variation (M24) and AM Rate Variation
(M22). ·Moving from right to left along the PCl axis are Rumbles, Roars, Barks,
Squeaks, then Trumpets and Squeals with the highest values for Lower Frequency
(M3), Upper Frequency (M4), Bandwidth (M6), Median Frequency (Ml 1), Frequency
of Peak Overall Intensity (M20), and Overall Entropy (M26) , and the lowest values
for Freq1:1ency modulation (M24) and Amplitude modulation (M22).
Squeaks show the least within-call variation along the PCl axis, so this call
type is the most stereotypic in its spectral structure. Trumpets show the greatest degree
of variation along the PC 1 axis, so this call° type is highly variable in its spectral
structure. These results are consistent with perceptual aural cues of lower and higher
frequency calls, and of stereotypy in the Squeak call. It is difficult to perceive rate of
change in modulation rates, so the results here provide differentiation that would
otherwise be missed. Frequency jumps were perceived in the Squeal and changes
tonality and noisiness were perceived in the Trumpet, so the results here for the
variability in Entropy are consistent with aural cues for these call types.
Along the PC2 axis, the variability is explained primarily by AM Rate (M21 ),
FM Rate (M23), and temporal parameters, namely Duration (M5), Median Time (M7),
Temporal Concentration (M9). There is some contribution by Lower Frequency (M3),
62
Frequency of Peak Overall Intensity (M20), and Upsweep Fraction (M28), but the
contribution is small compared to the other parameters, so these were not included in
the interpretation of the plot. The loading of AM Rate (M2 l) and FM Rate (M23) is
positive, so the value of these parameters increase along the PC2 axis. All other
parameters decrease along the PC2 axis. Moving from top to bottom along the PC2
axis are Squeaks with the lowest values for the temporal parameters, followed by
Barks, Trumpets and Roars, then Rumbles, then Squeals with the highest values for
temporal parameters.
Trumpets and Barks show the least within-call variation along the PC2 axis, so
these call types are most stereotypic in their temporal pattern Squeaks and Barks are
the shortest calls. Squeals and Rumbles include the longest calls and show the greatest
degree of variation along the PC2 axis, so these call types are highly variable in their
temporal pattern These results are consistent with perceptual aural cues of at least
duration.
Five of the 6 parameters used as decision rule in the classification were
contributors to the principal components. Only Up Sweep Fraction (M28) was not a
contributor, and this parameter was used only to differentiate a small number of
Squeak calls. In the classification tree, Lower Frequency (M3), Upper Frequency
(M4 ), and FM Rate Variation (M24) separated most of the higher frequency calls
(Squeak, Squeal, Trumpet) from the lower frequency calls (Bark, Roar, Rumble),
which is consistent with the separation along the PCI axis. In the classification tree,
Duration (M5) separated Barks from the other low frequency calls, Roars and
63
Rumbles. Roars and Rumbles were separated by Frequency of Peak Overall Intensity
(M20), which is supported by the separation along the PCl axis.
Analysis of Similarity (ANOSIM)
The classification showed a high rate of successful classification, and the
parameters that separated call types were consistent with the parameters that
contributed to the variance in the Principal Component Analysis, but neither of these
analyses determine if the difference among call types is significant. The null
hypothesis states that there are no differences in acoustic parameters among the six
basic call types (Bark, Roar, Rumble, Squeak, Squeal, Trumpet). A test statistic of
differences among groups was computed using an Analysis of Similarity (ANOSJM).
Global and pair-wise ANOSIM tests of the scaled dataset were run to determine if
there were significant differences in the acoustic parameters among the pre-defined
call types and between pairs of call types. Squeak and Squeal and Roar and Rumble
were sometimes challenging to differentiate using perceptual aural cues, and the
grouping in the PCA showed overlap of these call types. ANOSJM tests were used to
determine if these call types were significantly different or if they were gradations of a
composite call type.
The global ANOSJM test showed significant differences among the call types
(R=0.432, p<0.001), as shown in Figure 13. The pair-wise ANOSJM tests with a
Bonferroni-corrected alpha (a=0.0033) indicated a significant difference between
every call type, as shown in Table 9. Not surprisingly, the dissimilarity was greatest
between the Rumble and the higher frequency calls, Squeak (R=0.807, p<0.001),
64
Trumpet (R=0.731, p<0.001) and Squeal (R=0.675, p<0.001). The call types that are
challenging to distinguish by aural cues had mid-range R values, namely Squeal and
Squeak (R=0.425, p<0.001) and Roar and Rumble (R=0.417, p<0.001), so it appears
that these call types are indeed distinct. The most similar pairs were Bark and Roar
(R=0.227, p<0.001), Bark and Rumble (R=0.278, p<0.001), and Squeal and Trumpet
(R=0.230, p<0.001), which is consistent with misclassification rates in the
classification trees and the clustering overlap in the PCA plots. These calls were easy
to distinguish by aural cues, so it may be that the acoustic structure is more similar
across acoustic features that are difficult to perceive.
It is important to note that the group sizes were not balanced, so the statistical
power is low, and the pairs with low R values may not be significantly different in a
dataset with balanced group sizes.
R= 0432 P= <QQ01
i~ ~ ' '
I ' ' ' ' ' ' ' ' ' '
I I ' ' '
1 '
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' ' ' ' '
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~ '
~ ! ' '
~~ ' ' ' ' ' ' ' ' '
' ' ' ' ' ' ' ' : . ' ' ' ' 0 ----'--- , , ~ ~ ~
Between BRK ROR RUM SOK SQL TMP
Figure 13: Global ANOSIM test for difference in parameters among the call types. TMP:::Trumpet, RUM==Rumble, SQK=Squeak, SQL=Squeal, BLW=Blow, BRK==Bark, ROR==Roar Plot shows that the difference between call types is larger than the difference within each call type.
65
Table 9: Summary of pair-wise ANOSIM showing significant difference between all call types
Call pair R value Significance Significant Difference (Bonferroni-corrected a=0.0033)
BRKv.ROR 0.227 <0.001 Yes
BRKv. RUM 0.278 <0.001 Yes
BRK v. SQK 0.539 <0.001 Yes
BRK v. SQL 0.448 <0.001 Yes
BRKv. TMP 0.342 =0.001 Yes
RORv.RUM 0.417 <0.001 Yes
ROR v. SQK 0.685 <0.001 Yes
ROR v. SQL 0.453 <0.001 Yes
ROR v. TMP 0.468 <0.001 Yes
RUMv.SQK 0.807 <0.001 Yes RUMv.SQL 0.675 <0.001 Yes
RUMv. TMP 0.731 <0.001 Yes
SQKv. SQL 0.390 <0.001 Yes
SQKv. TMP 0.422 <0.001 Yes
SQLv. TMP 0:230 <0.001 Yes
Descriptions of call types by acoustic parameters
Acoustic measurements using Osprey yielded comparative measurements of
the basic call types (Bark, Roar, Rumble, Squeak, Squeal, Trumpet) and Blow. The
parameters selected for describing the calls types were those that were shown to be
most important in separating call types by the classification tree and PCA. Out of the
18 parameters that· were included in the analyses, 14 were shown to be important in
differentiating call types. Four of the parameters (Bandwidth (M6), Median Frequency
(Ml 1), Median Time (M7), and Temporal Concentration (M9)) were highly correlated
with other parameters and were not used by the classification tree, but were included
in the final description for completeness. Table 10 lists median values for these 14
parameters across the basic call types for quick comparison of acoustic structure.
66
Table 10: Median values of acoustic parameters shown by the classification tree and PCA to differentiate basic call types
for the inner 90% of the signal energy rather than th ~, - ----- - --r-.----
Bark Roar Rumble Squeak Squeal Trumpet
Lower Frequency (Hz) 75 124 26 495 1031 484
(M3)
Upper Frequency (Hz) 1065 777 665 1830 2896 4620
(M4)
Duration (sec) 0.5 1.5 2.3 0.2 1.9 0.5
(MS)
Bandwidth (Hz) 991 657 583 1318 1873 4091
(M6)
Median Time (sec) 0.22 0.66 0.98 0.10 0.75 0.24 (M7)
Temporal Concentration (sec) 0.22. 0.74 1.21 0.10 1.07 0.27
(M9)
Median Frequency (Hz) 408 322 165 912 1509 1305
(Mll)
Frequency of Peak Overall Intensity (Hz) 172 237 133 775 1443 1098 (M20)
AM Rate (Hz) 2.0 0.9 0.5 4.7 2.3 2.2
(M21)
AM Rate Variation (Hz) 0.029 0.046 0.372 0.012 0.023 0.023 (M22)
FM Rate (Hz) 2.3 0.8 0.5 4.9 1.5 2.7
(M23)
FM Rate Variation (Hz) 0.006 0.012 0.093 0.003 0.006 0.006
(M24)
Overall Entropy (Hz) 91 42 25 136 145 184
(M26)
Upsweep Fraction (%) 46 50 44 34 47 49
(M28)
Table 11 provides the final acoustic ethogram for the basic call types (Bark,
Roar, Rumble, Squeak, Squeal, Trumpet) plus Blow. This ethogram describes calls
both by aural cues and acoustic parameters. Values for Median Time (M7) and
Temporal Concentration (M9) in relation to Duration (M5) showed that energy was
67
distributed fairly evenly around the center of the call for all call types, so these values
were not included in the final ethogram. What we can say instead is that the energy is
distributed around the center of the call for all of the basic call types in the repertoire.
Upsweep Fraction (M28) was not included in the description because it differentiates
only a small number of Squeaks.
All Osprey measurements are made on the upper 90% of the call energy, so
range values may be greater for calls measured with signal processing tools that
measure extreme values.
Table 11: Final ethogram for Bark, Roar, Rumble, Squeak, Squeal, Trumpet, and Blow Measurements are for the inner 90% of the signal energy rather than the entire signal. The spectrograms are not the same frequency range or duration. The images were captures to show the ranQ:e of the oarticular call rvoe.
Call type and spectrogram
BARK Hz
8000
Definition
Aural cue: Sounds like a short loud grunt, clearing throat. cape buffalo. chuff of cow. cheetah. Ends abruptly with no roll off. Noisy compared to rumble.
Visual cue of caller: Mouth open.
Measurements of90o/o signal energy
Duration 0.1 - 1.3 sec (median 0.5)
26 - 240 Hz (median 75)
6000
4000
Lower Frequency
Upper Frequency
Bandwidth
Median Frequency
248 - 4963 Hz (median 1065)
129 - 4910 Hz (median 991)
101- 1182 Hz (median 408)
2000
0.0 0.1 0.2 0.3 0.4 0.5 0.6 times
Freq of Peak Intensity 37 - 840 Hz (median 172)
AM Rate 0 - 21.5 Hz (median 2.0)
AM Rate Variation
FM Rate
FM Rate V aria ti on
Overall Entropy
0- 0.186 Hz (median 0.0.29)
0 - 24.8 Hz (median 2.3)
0 - 0.046 Hz (median 0.006)
17 - 468 (median 91)
68
"'
4000
3000
2000
1000
00
GOO
500
""'
""' i '
?00 ~
l 100
I
Call type and spectrogram
0.5
0.ti l 0
ROAR
1.0 1.5 hme s
RUMBLE
'' ,,,.. ,
2.0
'l.t 3.0 J .~ I
Definition
Aural cue: Sounds like a lion roar with changing pitch Pitch sounds like it changes because of amplitude modulation. Perceived as low frequency.
Visual cue of' caller: Mouth wide open or widens during call. lower abdomen contracts.
Measurements of90% signal energy
Duration 0.5 - 3. 7 sec (median 1.5)
Lower Frequency 23 - 361 Hz (median 124)
Upper Frequency 210 - 3763 Hz (median 777)
Bandwidth
Median Frequency
Freq of Peak Intensity
AM Rate
AM Rate Variation
54 - 3650 Hz (median 657)
122 - 1214 Hz (median 322)
40 - 1292 Hz (median 237)
0.3 - 4.3 Hz (median 0.9)
0.012 - 0.464 Hz
FM Rate
FM Rate Variation
Overall Entropy
(median 0.046)
0.3 - 6.8 Hz (median 0.8)
0.012- 0.232 Hz (median 0.012)
11 - 208 (median 42 )
Aural cue: Sounds like a diesel engine. motorboat. distant helicopter. Pulsating with a flat pitch. Perceived as low frequency .
Visual cue of caller: Mouth visibly open, forehead flutter.
Measurements of90% signal energv
Duration 0.9 - 10.0 sec (median 2.3)
Lower Frequency 14 - J 20 Hz (median 26)
Upper Frequency 104 - 2498 Hz (median 665)
Bandwidth 86 - 2422 Hz (median 583)
Median Frequency 26 - 590 Hz (median 165)
Freq of Peak Intensity 17 - 750 Hz (median 133)
AM Rate 0.1 - 3.6 Hz (median 0.5)
AM Rate Variation 0.023 - 2.972 Hz (median 0.372)
FM Rate 0.1 - 2. 7 Hz (median 0.5)
FM Rate Variation 0.023 - 0.929 Hz (median 0.093)
Overall Entropy 4 - 133 Hz (median 25)
69
Call type and spectrogram
SQUEAK
8000
6000
4000
2000
0.0 0.1 0.2 0.3 0.4 11me. s
Definition
Aural cue: Sounds like a wet finger across tight rubber surface. audible pedestrian crossing signal. baby alligator. gibbon call. High-pitched sound that falls in pitch. powerful initial sound.
Visual cue of caller: Cheeks depressed. mouth open slightly or closed. tail sometimes erect.
Measurements of 90% signal energv
Duration
Lower Frequency
Upper Frequency
Bandwidth
Median Frequency
Freq of Peak Intensity
AM Rate
AM Rate Variation
FM Rate
FM Rate Variation
Overall Entropy
0. l - l.2 sec (median 0.2)
194 - 883 Hz (median 495)
797 - 3 768 Hz (median l 830)
301 - 3273 Hz (median 1318)
538 - 1905 Hz (median 912)
537- 2024 Hz (median 775)
0.8 - 44.9 Hz (median 4. 7)
0.003 - 0.026 Hz (median 0.0.012)
l.l - 70.5 Hz (median 4.9)
0.003 - 0.032 Hz (median 0.003)
58 - 294 Hz (median 136)
70
Call type and spectrogram
SQUEAL
8000
~ /;' 6000
• ! """' 2000
0000
~
/;' 6000
l """' 2000
0.0 05 LO " 2.0 time!>
Definition
Highly variable in strucrure and duration. Variations numbered in aural cues.
Aural cue: ( l) Short utterance . Sounds like rubber shoes on a gym floor. release of air out of a balloon. windsrueld wiper on a dry window. a dog whine. (2) Train of repeated Squeal with short breaks between sounds. Sounds like squeals in staccato. intermittent release of air from a balloon. Each squeal sounds flat in pitch but they jump up and down. (3) Sounds like continual release of air from a balloon. fingers on a wet balloon. sliding pitch. sounds long.
Visual cue of caller: Cheeks depressed. mouth open slightly or closed.
Measurements of 90% signal energ'' ·
Duration 0.1 - 16.5 sec (median 1.9)
Lower Frequency 226 - 1927 Hz (median 1031)
Upper Frequency 851 - 8193 Hz (median 2896)
Bandwidth 129 - 7816 Hz (median 1873)
Median Frequency 575 - 2384 Hz (median 1509)
Freq of Peak Intensity 422 - 2218 Hz (median 1443)
AM Rate
AM Rate Variation
FM Rate
FM Rate Variation
Overall Entropy
0.1 - l 2.3 Hz (median 2.3)
0.006 - 0.087 Hz (median 0.023)
0.1 - 23.6 (median 1.5)
0.006- 0.041 (median 0.006)
35 - 479 (median 145 )
71
Call type and spectrogram
TRUMPET
8000
6000
4000
"""'
0.0 02 " 0.6 0.8 10 .., '-'
htne ~
BLOW
"'°"" " 15000 '
'J
"' ~10000 1
! 5000
- - . -- -
0 - -· -QC 02 0.4 06 °' 1.0
"
1 2
Definition
Aural cue: Sounds like a trumpet blast. Perceived as a higher frequency call.
Visual cue of caller: maybe a lifting at base of trunk. trunk sometimes extended. tail sometimes erect sometimes (any behavior indicating extreme arousal).
Measurements of'90o/o signal energv
Duration 0. 1 - 2.4 sec (median 0.5 )
Lower Frequency 54 - 980 Hz (median 485)
Upper Frequency 128 1 - 8926 Hz (median 4620)
Bandwidth
Median Frequency
Freq of Peak Intensity
AM Rate
AM Rate Variation
FM Rate
FM Rate Variation
Overall Entropy
1012 - 8742 Hz (median 4091)
146 - 4454 Hz (median 1305)
129 - 353 1 Hz (median 1098)
0.4 - 27.6 Hz (median 2.2)
0.006- 0.061 Hz (median 0.023)
0.4 - 23.l Hz (median 2.7)
0.006 - 0.244 Hz (median 0.006)
29 - 83 l Hz (median 184)
Aural cue: Forced exhalation through end of trunk. Not a vocalization.
Visual cue of caller: air blast from trunk
Measurements of'90o/o signal energy
Duration 0.3 - 1.2 sec (median 0. 7)
Lower Frequency
Upper Frequency
Bandwidth
Median Frequency
81 - 1512 Hz (median 372)
1448 - 9318 Hz (median 3790)
1217 - 8990 Hz (median 3273)
531 - 4021 Hz (median 1156)
Freq of Peak Intensity 97 - 3295 Hz (median 894)
AM Rate 0.8 - 3.6 Hz (median 1.4)
AM Rate Variation 0.012 - 0.07 Hz (median 0.046)
FM Rate
FM Rate Variation
Overall Entropy
0.8 - 25.1 Hz (median 1.6)
0.012 - 0.046 Hz (median 0.012)
102 - 875 Hz (median 286)
72
Nonlinear dynamics of vocal production
These basic call types exhibited some nonlinear dynamics of vocal production,
which include subharmonics, deterministic chaos, bifurcations, biphonation, and
frequency jumps (Mann et al., 2006). Subharmonics are sounds at frequencies other
than the fundamental frequency or integer harmonic of the fundamental that can be
generated by coupled oscillators. Deterministic chaos comprises noisy signals that are
not random noise, but generated by chaotic process. Bifurcation is the rapid shifts
between tonal harmonics and chaos. Biphonation is the generation of two independent
frequencies simultaneously. Frequency jumps are abrupt up or down transitions
between two frequencies or harmonics that are unpredictable (Mann et al., 2006). The
Trumpet exhibited clear evidence of bifurcation, as measured by the Overall Entropy
(M26) parameter and by visual inspection of Trumpet spectrograms. The Squeak and
Squeal also exhibited evidence of bifurcation as measured by the Overall Entropy
(M26) parameter, but it was not evident from visual inspection of the spectrogram.
The Roar and Bark appear to be noisier sounds, but the shift from linearity to non
linearity is not evident. The Squeal exhibited clear frequency jumps when produced in
a train of Squeals. The degree to which these non-linearities vary with perceived level
of arousal is being investigated, but is not included here.
Comparison of low frequency calls to background noise
Power spectra of Paired Rumbles and noise for the three study sites were
compared to investigate the potential for Rumbles being buried in background noise.
At the Oregon Zoo (Figure 14) the Rumble had multiple peaks below 200 Hz. The
73
noise floor started to rise below 300 Hz, with the peak below 1 OOHz. From visual
inspection, the signal-to-noise ratio was less than 10 dB below 100 Hz, but increased
in the 100-300 Hz range. Given this ratio, it appears that signal energy below 100 Hz
could become inaudible due to noise in this environment. In the entire range of 15 Hz
to 20 kHz, noise declined more sharply from lower frequencies to approximately 2
kHz, then declined gradually or flattened. In one recording there was a slight increase
in noise in the 6-8 kHz range.
At the Elephant Nature Park in rural Thailand (Figure 15), the Rumble had
multiple peaks below 200 Hz. The noise floor started to rise sharply below 100 Hz,
with the peak below lOOHz. Noise had the highest power at low frequencies (below
approximately 30 Hz). From visual inspection, the signal-to-noise ratio was less than
10 dB below 100 Hz, but increased in the 100-300 Hz range. Given this ratio, it
appears that the signal energy below 100 Hz could become inaudible due to noise in
this environmentas well. Wind gusts were a factor at this site. In the entire range of 15
Hz to 20 kHz, noise declined sharply from low frequency to approximately 400 Hz,
then declined gradually across the range. In all selected recordings there was a notable
increase in noise in the 2-4 kHz and 6-11 kHz ranges. Based on aural cues at the time
of observation, noise in 6-11 kHz band may be sounds of insects or other arthropods.
At the Royal Elephant Kraal in urban Thailand (Figure 16), the Rumble had
multiple peaks below 200 Hz. The noise floor started to rise below 300 Hz, with the
peak below lOOHz. From visual inspection, the signal-to-noise ratio was less than 10
dB below 200 Hz (except for recording at close proximity), and remained fairly low to
74
about 300 Hz. Given this ratio, it appears that signal energy below 200 or 300 Hz
could become inaudible due to background noise in this environment as well. Wind
gusts were a factor at this site. In the entire range of 15 Hz to 20 kHz, noise declined
sharply from 15 Hz to approximately 400Hz, then declined gradually across the range.
In all selected recordings there was a notable increase in noise in the 6-11 kHz range.
With the exception of the increase in the 6-11 kHz range, the noise profile across the
entire range of 14 Hz to 20 kHz looked more similar to the Oregon Zoo than to the
Elephant Nature Park, so the frequency band of the background noise maybe
primarily a result of the degree of urbanization.
.,, ~
iii 80
i 75
fH" 70
65
60
Rumble: 14 Hz to 1.2 Hz
V'v·A~
55 1m 2m 300 4m 500 60J 700 ooo 900 1000 1100
frequency, Hz
Noise: 14Hzto1.2 kHz 100
95
90
85 .,, g
80
j 75
fli 70
65
60 A •. ~r'V-w.,.,A 1 ~W\./W •V'v\r1;.
55 200 400 600 800 1000 1200
frequency, Hz
.,, ~
90
g 85 ~
~ g 80
fli 75
70
Rumble: call bandwidth
ss~~-~-~-~-~-~-~-~-~-~ 60 m ~ ~ B D ~ 400 ~ 500
frequency, Hz
Noise: 14 Hz to 20 kHz 100.---.,.--~--.,.--~----~-.,.--~-~-~
00
BO
20 2oo:i 4000 6000 8000 10000 12000 14000 16000 18000
frequency, Hz
Figure 14: Power spectra of Rumble and background noise at the Oregon Zoo Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
75
Rumble: 14 Hz to 1.2 Hz fllr-~~r-~~~~~~~~~~~~~~~~
75
" g BJ ~
~ 55 §
~ 50
45
40
35 ~ ~ 200 400 500 ~ ~ 1200
frequency, Hz
Noise: 14 Hz to 1.2 kHz lllr-~~~~~~~~~~~~~~r-~~~---.
75
70
66
40
~" 35
:J:J 100 200 m 400 500 500 100 soo oo:i 1000 1100 frequency. Hz
Rumble: call bandwidth BOr-~~~~~~~~~~~~~~~~~~--,
'll g 65
" ~ 5_ 60 lg
/\ 55
50
45 ~~40~-,60~~00~~,oo".:-~,20~~,40~~,60~-,-,eo".:--=-200".:--=-220._,_~
frequency, Hz
Noise: 14 Hz to 20 kHz 80r-~~~r-~r-~~~~~~~~~~~~--,
70
60
50
m = == ~1=1~1=1=1~ frequsncy, Hz
Figure 15: Power spectra of Rumble and background noise at Elephant Nature Park Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
76
Rumble: 14 Hz to 1.2 Hz 85~~~~~~~~~~~~~~~~~~~~---,
80
70 .., g Ei5 ~.
j ro
i55 50
45
40
35 200 400 600 800 1000 1200
frequency, Hz
Noise: 14 Hz to 1.2 kHz 10~~~~~~~~~~~~~~~~~~~~
60
l 55 ~
-! ro §
~ 45
40
35 v~ 3J 100 200 30'.l 400 500 600 700 000 900 1000 1100
frequency, Hz
.,, . 75
70
f 65
i § 60
!li 55
50
45
Rumble: call bandwidth
40 ~
.,, . ~ c ~
100 200 300 400 500 600 frequency, Hz
Noise: 14 Hz to 20 kHz 10~~~~~~~~~~~~~~~~~~~---,
60
50
i 40
ai .., 30
20
10 = ~~ ~1~1=1~1~1~
frequency, Hz
Figure 16: Power spectra of rumbles and background noise at the Royal Elephant Kraal Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
77
CHAPTER V: DISCUSSION
The goals of contributing to the basic science of elephant communication were
met by the results of this research. An acoustic repertoire of Asian elephants based on
acoustic parameters was defined. A comparison of how the repertoire was used by
groups of elephants and individuals was made. The manual methods of classification
based on perceptual aural cues and visual inspection of spectrograms was compared to
automated classification, and structural distinction among call types was validated.
The fact that a limited number of acoustic parameters differentiated call types suggests
that these parameters could be used for detection of elephant calls. The next steps
would be to determine if the parameter set could be reduced further, and to examine
differentiation of elephant sounds from non-elephants, the data of which has already
been collected and is ready to analyze. Finally, the descriptions of elephant sounds
presented in this thesis serve as a basis for comparisons among captive and wild Asian
elephants and between Asian and African elephants.
The final repertoire was defined by 6 basic call types (Bark, Roar, Rumble,
Bark, Squeal, Squeal, and Trumpet), 5 call combinations and modifications with these
basic types as their constituent parts (Roar-Rumble, Squeal-Squeak, Squeak train,
Squeak-Bark, and Trumpet-Roar), and a sound that was produced frequently by many
elephants, the Blow. Results suggest these call types are differentiated by 11 temporal
and spectral parameters, and with future analyses this feature set may be able to be
reduced further.
78
Given the high success rate of the classification tree using only 6 parameters as
decision rules, it appears that this tree does have potential as a predictive model for
classifying new calls into pre-defined call types. Consistent with aural cues was the
separation of the higher frequency calls (Squeak, Squeal, and Trumpet) from the lower
frequency calls (Bark, Roar, Rumble), and the confusion between Roars and Rumbles.
Separation of the call types with the Principal Component Analysis appeared to be in
agreement with the separation by the classification tree. Finally, analysis of similarity
tests showed significant difference among the 6 basic call types and between all pairs
of these call types, so these call types are structurally distinct.
Data used in this study were collected in both urban and rural settings with
many sources of noise and recorded with multiple recording systems, and results
suggest that automated call detection is possible in varying recording situations, with
various noise sources and using various recording systems.
Biological sources of variability within call types may include individuality of
the caller, social context, level or arousal and state of motivation, and potentially the
size of the animals and size of the head space. Some call types appear to be stereotypic
in either temporal or spectral structure and other vary widely. The Squeak is a
stereotypic call, the sound production of which includes manipulation of the cheek or
lips. The Squeal is highly variable, but the sound production appears to be the same.
Little is known about sound production in elephants, but it may be that these sounds
are produced by s.ome source other than the vibration of vocal cords alone.
79
Results of repertoire usage agreed with (Langbauer, 2000) in that there Was a
difference in calling pattern of males and females, with male elephants producing
fewer call types than their female counterparts (Langbauer, 2000). The repertoire
defined by this study suggests some differences between African elephants and Asian
elephants. The most common vocalization of African elephants is the Rumble, which
is highly variable and is used in multiple contexts and serves multiple functions (Soltis
et al., 2005; Olson, 2004). The Rumble is produced by Asian elephants, but it was not
the most common sound produced in this study. The Asian elephants in this study
were captive or domesticated and were not wild, but the published calls of wild
elephants by McKay (1973) suggest that at least some of the call types defined in this
study are also produced by wild Asian elephants. Ecological differences in populations
of African and Asian elephants include predator pressures, resource availability, group
size, and human encroachment. Home range size may be a function of these
differences, and communication modalities and distance may be a function of home
range and group size. One could hypothesize that a reason for the Rumble being the
primary call of African elephants is a greater need for long distance communication in
order to maintain separation while remaining in contact for the purpose of resource
utilization.
Future analyses for this dataset include completing the caller identification,
investigating the communicative function of these sounds based on behavioral data
already collected, investigating potential explanations for differences in call
distribution among individuals, running unsupervised call classification as another
80
validation of established call types, doing a more rigorous analysis of signal-to-noise
ratio at various frequency values, and running non-elephant sounds through a
classification tree as a preliminary test for call detection potential.
81
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86
APPENDIX A: DISTRIBUTION PLOTS OF PARAMETERS
IDSTOGRAMS AND BOXPLOTS OF ORIGINAL DATA
All of the histograms and boxplots below include. outliers. The summary statistics exclude outliers removed. Plots are included only for the 18 parameters that were used in the statistical analysis.
The figure panels are as follows: A) Histogram of parameter for all call types combined. B) Histogram of parameter for each call type. C) Summary statistics for all call types combined. D) Box plot of parameter for all call types combined. E) Boxplot of parameter for each call type.
Abbreviations for the call types are as follow: TMP (Trumpet), RUM (Rumble), SQK (Squeak), SQL (Squeal), BL W (Blow), BRK (Bark), ROR (Roar).
A.
20
~ 15 0 l-o i t-0
~ ll.
500 1000 15&D 2000
dta.6$LowerFreqM3
B.
"" .. " 40
21)
100
" .. " "
0 SOO 100tl 1SOG :2000 o soe 1000 1soo 2ooe
D.
c ;;;
. ,,.. "
"' 1000
Lowt'f'FreqM3
is~
E.
.., ;;
"' ~ ., !: 0 -'
""
d!a6$LowerF"'qM3
2000
1500 ·--i
' 1000 '
D 500 0
' __ @ __
~ c.tJ BLW BRK ROR
Figure 17: Histograms and boxplots of Lower Frequency (M3) TMP and SQL more variable, other call types more stereotypic.
a::;
RUM
cau
·c.
LowerFreqM3 Min. : 14 .13 lstQu.: 188.42 Median : 495.26 Mean : 532.77 3rd Qu.: 721.36
'"' 1 Max. : 1927. 22
" " " "
• . 0
-~!"--
4=J 0
0
SQK
:~-] I•
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[..-: ' rj' ' '
__ J._ __
' '
SQL TMP
87
A.
"
" ! 1~ .. ~
! "
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~
"
.<000 JOOD 5000
dta 6Sl/ppe!'FreqM4
.
2CIOO 400Q ~
UpperFieqM4
B.
~ lo t' .. e " a.
60
40
20
60
... OIR.• : 'U :>~soli<:' "Y~:lE:;H: u:sai:;y;: · ·
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.-....,.~. > ,;.-, ,.
"'""
a 2000 6COO 10000
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8000
" :::. 6000 O' ., ~ ., ! 4000
2000
,.........., i I
I • r
1 I y
Bl.W
~ __ .!.~-
BRK
0 21JOO 6000 10000
·····~·-· c:p ~ ROR RUM
cau
Figure 18: Histograms and boxplots of Upper Frequency (M4)
60
40
20
c.
UpperFreqM4 Min. : 103.6 1st Qu. :1341.8 Median :2261. 0 Mean :2780.7 3rd Qu.:3434.5 Max. : 9318. 5
' ' ' ' '
rLJ i
1 • L,.._J
Q •
__ , __ !_ __
SQ)( SOL TMP
ROR and RUM have narrow range. TMP, SQL, and BL W highly variable, which may be partially a function of signal strength (distance to caller).
88
A.
"" 11111
l:JL
D.
J ~
1~
a:a.6$0ISabonM5
n, __ .. DuJauonM5
B.
100
80
60
H 40 20
0
I 1U 0 f-0 c .. ~
" Q_
100
80
60
" I 4l)
20
.. "
;.{H:t'if;:~'ii'MP.?:t>:\q;.~
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dta6$DurationM5
E.
15
"' :::< 10 c 0
~
e ~ [~] ~ ~~
__ ., __
BLW BRK ROR RUM
Call
Figure 19: Histograms and boxplots of Duration (MS)
100
80
60
40
20
.J..,.
SOK
c.
DurationM5 Min. : 0.067 1st Qu.: 0.331 Median : 0.737 Mean : 1. 292 3rd Qu. : 1. 5 7 9 Max. : 14. 820
_J :
Q :.l i::;;:Q.
SOL TMP
ROR and RUM overlap in perception. SQL variable in duration. SQK, BRK, and TMP stereotypic.
89
A.
! " " j
"
D.
l3 ~
""'
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'''" BandwidthM6 ""
B.
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,--•:::i
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can
Figure 20: Histograms and boxplots of Bandwidth (M6)
6-0 50
c.
BandwidthM6 Min. : 53.83 1st Qu.: 904.39 Median :1682.08 Mean :2247.91 3rd Qu. :2802.61 Max. : 8990-11
'
J_ --~--
n y~
'
' ,---:--,
~~~T~-'. 8
SQl< SQL TMP
RUM mostly low frequency (as expected). BLW and TMP have very high bandwidth and variability, which may partially be due to distance to source. Squeal has relatively high bandwidth and very high variability. SQK has relatively wide bandwidth, but it is stereotypic.
90
A.
.. ! ~ .. j "
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. ~
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0
10 10
dta.6$MedianTimeM7
"''i''"-
_J __ ct 0 g::: Si~.?
__ ... __ ~
BLW BRK ROR RUM
Call
Figure 21: Histograms and boxplots of Median Time (M7)
10
80
60 40
20
c.
MedianTimeM7 Min. 0.0210 1st Qu.: 0.1340 Median : 0.3245 Mean : 0.5950 3rd Qu.: 0.6773 Max. :10.5830
• • --l-
.1. ~ e i:iik~
SQK SQL IMP
91
A. B.
100
80
60
40
20 0
1 .. i I r I 2 c >-0 c .. e ..
Q.
100
60
60
40 ~ 6STtmeConcer.1M9 I 20
™"".'''''
L • RUMii>i"''' : SOK
IL L i BLW BRK
L L
SOL
kn_ '.; ROR
L
c.
TimeConcentM9 Min. : 0. 0000 1st Qu~: 0.1340 Median : 0.3370 Mean : 0.7946 3rd Qu. : 0. 8590
100. Max. :13.8450
80
60
40
20
10 15 10 15
D.
s j[ ! ~· ~---~--"" <>->-»->.>
~
~L
" TimeConcentM9
E.
"' 10
~ ~ c 0 0
.~ 5 >-
dta. 65TimeConcentM9
--~--
-···y .. •·
[;] ~ ~~·;".J ~ BLW 8RK ROR RUM
Gall
Figure 22: Histograms and boxplots of Temporal Concentration (M9) SQL highly variable. RUM more variable than others.
~ --~--
'
.4- LJ --~-~
SQK SQL TMP
92
A.
! 60
" l
D.
!!
~ .:
..
....
-ea a..~
ala.€STimeAs)'mrr.M10
I , --·~ ~ i
~:J.l! '·' 0.2
TimeAsymmM10
B.
100
80
6(}
40
20
0
~ I-0 c .. e .. "-
100
60
60
40
20
... "
c.
%',:·:: ':TMP TimeAsymmMlO
l_ Min. :0.39400 1st Qu. :0.00000 Median :0.00000 Mean :0.0006586 3rd Qu. :0.00100 'liir= ,070600
\&:\fr i BlW H''.lE::.\BRK ROR
1-l~ll_ I I i I I I I I I I I I I I I I I ( l I
-0.4 O.Q 0.20.40.60.B -0.4 0.00.20.40.60.8
dta.6$TimeAsymmM10
E.
0.6
0 0.4
~ E
! 02 ~ .. .
0 ~ 0 . -+ + -t-E o.o -+- + ~ .........
i= 8 >
-0.2
-0.4
SlW SRK ROR RUM SOK SOL TMP
Caff
Figure 23: Histograms and boxplots of Temporal Asymmetry (MlO) Stereotypic with minimal variability.
93
A.
"
! ,, " i Q_"
D.
~
"
1000 2!:-00 ::000
dl:t 6.SMediatlF reqM 11
~- -- ---1 ' t-------lLJ · ---·-·-··--····L. 0
: l : : ~ :
1000 """ '""' MediarFreqM11
B.
'"'
~ l-o c " re ..
Q_
60
40
20
60
40
20
'IEi'~;
;~;;.··
LIL 0 1000 3000 0 1000 3000
dta6$MedianFreqM11
E.
4000
i 3000
i-j 0
.. -r .... 0
1000 . s . c!J ~ ~;:~
BtW BRK ROR RUM
Call
Figure 24: Histograms and boxplots of Median Frequency (Mll)
60
40
20
3 .
c.
MedianFreqMll Min. : 26. 24 1st Qu.: 663.81 Median :1046.76 Mean :1105.67 3rd Qu. :1489.14 Max. :4454.33
. . . . 8 [J i •
cb Ll --g-----L-.
SOK SOL ThlP
94
A. B.
6()
40
20
0 .....
i')'.t!ii:\•iRVM ·-· ! JO >;
! !T."
~
I f-
_l_ 0 1:
aL .. ~ Cl.
" r.; .. : .. •.: BLW BRK
" eta 6$F'"&!'.;AsyrnmM14
60
_lj 40
~,___._ 2D
I I ! I I I I
-0.5 0.0 05 -0.S 0.0 0.5
dta. 6$FreqAsymrnM 14
D. E.
g 0.5 4 --:--
' ' ' ' ' ... 0 ' '
:1 ··-1··
GJ 8 E n ~ I I I E GJ L!J . -1·----· . «<•<>:•!!<>< .f o.n
0- . ~ ' u. ' '
··:.
-0.5 -G.5 00 " "eqAsymmM14
' BLW BRK ROR RUM
Cal
Figure 25: Histograms and boxplots of Frequency Asymmetry (M14)
[ 60 40
2D
--?--
(j-1 . ; .~
--6--.
SQK
c.
FreqAsymmM14 Min. :-0.63400 1st Qu.:-.00600 Median :0.02800 Mean :0.05637 3rd'Qu. :0.12400 Max. : 0. 66600
,
~ I --~ -·-
4::J --~--
0 __ ._ __
SOL TMP
95
A.
! 10
j
tll.6$?1(QveralR~18
D.
~
"
,. .. .. PkOverallReffM18
"
B.
~ 0 c ., l: ..
Cl.
25 20 15
10
5
25 20 15 10 5
, ..
,,,lifV !TMF':0',S'.1;1
0 20 40 60 80 100 0 20 40 60 so 100
dta,6$Pk0vera11RelTM18
E. 100
--i--
' BO
' "' ' :ii
'
LJ !:: 60 ..
~ Q:; ~ ! • .,
40 > 0
"" Cl.
20 ' ' ' ' ' ' ' 0
--i--
Bl'IV 8RK ROR RUM
Gaff
25 20
15 10
5
c.
PkOverallRelTM18 Min. : 0.00 1st Qu.:23.50 Median : 41. 50 Mean : 41. 89 3rd Qu. : 5 7 . 7 3 Max. : 99. 63
--;--' ' ' ' '
D~ SOK SOL
•
1MP
Figure 26: Histograms and boxplots of Relative Time of Peak Overall Intensity (M18) High variability within call type, but similar among call types.
96
A.
% ~ $
i .r"
D.
<'l ~
B.
~ I-0 c .. ~
" Q.
dta.6$PkOYeralFM2o
1000 ""' 3000
PkO\'el'allFM20
00
60
40
20
0
ao 60
=~ ill 0 1000 2000 3000
E.
3000
§ !!,, 2000 ~
§ Q.
1000
K sot'"·
LIL d1a.6SPkOVeraHFM20
L:J
BtW
--1-· GJ BRK
0 1000 2000 3000
e ~~-~~ w
ROR RUM
Galt
60
60
40
2\)
0
'
c.
PkOverallFM20 Min. : 17. 50 1st Qu.: 481.71 Median : 861. 33 Mean : 931.14 3rd Qu. :1346.23 Max. :3531.45
' '
Q ~ lTJ __ i.._.
SQK SQL TMP
Figure 27: Histograms and boxplots of Frequency of Peak Overall Intensity (M20)
97
A.
"
! ~JO
~"
D.
~ ;;;
11·1
lO
dla 6SAMRa!et.121
" f ' w,---.--»-~
" " AMRa!eM21 " ..
B.
~ r-0 c ., ~ ..
CL
100
80
~~'
~~L_
c.
AMRateM21
LfLILt~ ROl'Ui: -
Min. : 0.000 1st Qu.: 1.118 Median : 2.207 Mean : 3.053 3rd Qu. : 3 . 7 4 5 Max. :44.939
BLW BRK
~lLLL 010203040
E.
~ ~
4V
30
ll'. 21l
~
10
dla.6$AMRateM21
_J __ ~
Bt.W
.L r.::::P
BRK
010203040
. __ _. __ c::b
ROR
~ RUM
Call
i __ L
C!J ' ~~~ L-'--. ..i
. --V-- --'--
SOK SOL TMP
Figure 28: Histograms and boxplots of AM Rate (M21)
98
A.
~ ~
j~
D.
~
"
llilllll
dd.6SAMRateVa11-R2
f i il~ . .,, ><·<'
ii ~ i
'·' B " 1.5
A.\1RafeVarM22
B.
100
80
6-0
40
20
0
~ I-Ci c "' ~ ., 0.
100
80
60
40
20 0
" " 3.0
c RUM t'. SOK k' <::ru
L[L/L
A.
~ ~ 4~
i
D.
~ ~
:ta.6$FMR.l!eY2l
:11 ; ' :J· -- +--0 ~. 'LJ :
"' " FMRateM23
B.
100
80
60
40 20
~ f-0 c ., ~ ., "-
1-00
80
60
40
20
..
c.
i~~ea~71:~~~"~ FMRateM23
L Li LtE
Min. : 0.0000 lstQu.: 0.9653 Median : 2.2160 Mean : 3.7613 3rd Qu.: 4.4170 Max. :70.4720
BtW BRJ'<k~:::::.;;: ROR
LILIL 0204060
E.
"' N
60
i 40
C! a: 20
0
dla.6$FMRateM23
0 0 0
~ 0 --1'--
~ ± BLW BRK
0 204Q60
' ·---~--- Ml~ ~
ROR RlJM
Call
i --~-- 0 •
-l- _ _; __
~ ci;:J c:t::i SOK SQL TMP
Figure 30: Histograms and boxplots of FM Rate (M23)
100
A. B. c.
Hi TMP FMRateVarM24 100
L Min. :0.00000
so 1st Qu.:0.00600 60 Median :0.00600 40 Mean :0.02094 20 .. 0 3rd Qu. :0.01200
,. RUM ll! "rn Max. :0.92900
~ L L 100 .... 60 i' t-
~ 0 60 ~ ~ ~ c
~ 40 ..
e 20 Q)
"' a. 0
i/ Bl..W BRK .. ROR
- 100
L L ' so
L ... ., " .. . .. ,, 60
~.GSFMR.i.!e'lart'.424 40 20
0
0.0 02 0.4 0.6 0.8 1.0 0.0 0.2 04 0.6 0.8 1 0
dta .6$FMRateVarM24
D. E. ¢
o.s 0
" ~ 0.6 Ill
~ - .• ' '0 ·• .; ~ . < ~ 0.4 0
t: ::;, LL ··-r--,, .
0.2 ., <> ii
--T-- I • ; D.D {i.2 '·' o.e "
8 ~ c~:J
l.._.,.. _ _I _._ --1-- _j_ 0.0 -·--FMR8teVarM24
BlW BRK ROR RUM SOK SOL IMP
Call
Figure 31: Histograms and boxplots of FM Rate Variation (M24)
101
A.
,, ! " J :0
D.
.. ~ ;;:
"' tia.6$Eri!ropyt.126
; ·-----~-1·~·><r..,.<-«flN>~·;<o
200 4C-O "' EnD"opyM26 "'
B.
~ IC;
c "' !,>
&'.
80
&ll
4l)
20
b'ff'.z".~1MF:'n ·.
_, . .,,, I -:j _L.__ I
1 I 1 I I I I I I I
0 200400600 800 I I
dta 6$EntropyM26
E.
1000
• =i ' ' ' ' '
l 400 [] 200 -1
' __ ... __ '
G.J "'" -BLW BRK
rL I I I l I l
0 200400600 800
--7--
4;1
ROR
. --~-l±:J
RUM
Call
Figure 32: Histograms and boxplots of Overall Entropy (M26)
80
60
40
2{)
c.
EntropyM26 Min. : 3. 968 1st Qu.: 79.487 Median :136.609 Mean :159.961 3rd Qu. :194.310 Max. :1000. 792
--,--
g --~--
GJ a; cb --1.-- .... .:. ...
SQK SQL TMP
Highly variable among call types. TMP has highest variability (tonal-noisy variability), which may reflect level of arousal in switch to noise. Wider bandwidth calls have higher entropy. Chaotic noise has been perceived in the calls with higher frequency ranges (roar, trumpet, squeals), so this is consistent with perceptual aural cues.
102
A.
! . " j
D.
~ 'ii:
dta..65UoswiiFracM28
; ! :
B.
]! 0 f-0 c "' " "' Q_
; Di''···,,,., ... ! .:•<> -----r---------1 . • ---------r·<. .JN~ :;.;.<
' I ' ; 1 :
"' " .. " UpswpFracM28
,ii{1!ki~ll'~Pi :v,::,ij
RL ;>r,,:;SQL
_lJ~
lLl_tJ .~ .. 1 0 ' '
"'
0 2Q 4() 60 80 100 o 20 40 60 ao 100
ctta,6$UpswpFracM28
E. 100
SG
a:!
"' ~ ·1 ~ u.. C,
~ 40 0,
=>
20
--7--
~ [5 ..... l
'
BLW BRK ROR
--r-C;J
'
-+-RUM
Call
Figure 33: Histograms and boxplots ofUpsweep Fraction (M28)
c.
UpswpFracM28 Min. : 6 .196 1st Qu.: 40.340 Median : 46.450 Mean : 44.931 3rd Qu.: 51.132 Max. :100.000
--:--r-i I • I ! ! L-,-1
SQK
--r-lfJ -··i--
SQL
--T--
' [~]
TMP
103
Q-Q PLOTS OF SCALED DATA
In a Q-Q plot, the x-axis is the expected value for a normal distribution and the x-axis is observed. The data are ranked and the quartile is calculated. If data are distributed normally then the shape will be a straight line where y=x.
M 2 ::>
l ~ 0 ' i ~
.,
·2
<JlO!l11
;,>Cc' ~ .. ,,.
Figure 34: Q-Q plot of Lower Frequency (M3)
1 ~
i' ~
-1
·2
. '
/ I
(JlOrm
Figure 35: Q-Q plots of Upper Frequency (M4)
~ ~
l \:: U> J
~
i ~
.,
~ i 3
~ '
-1
-3 -2 ·1 G 1 2 J
"J
"I /l.L/ .. ,.· -~ f--1 ..,.,,,,-- . . ' . , <' ... :BRK:';·!'.<'.".'"dli".'J'.ROR·::;·.:-->:Tc• !'.'.'hlUJl,F
.,~· .,.~~· ~·· -3 -2 -1 G ,1 2 3 .J -2 -1 0 1 2 3
qnurm
·3: -2 -1 0 1 2 3
b'::. •-:t::.S:OJ< !) ·;"L'..·•<''.•JMFl <.d '.·
-1
(" /,•' .~··1 .. / ,,
:oo<"·~ ~+(t~~ b~/-~f~'.:'::1u1u~·,._, ,,,,.,'T
,_,.!' <00___.. .~ -3 -2 -1 0 1 2 -3 -2 -~ I) l 2 3
qnorm
104
"' " g .!! •
i ~
I
·2
C/l(lml
Figure 36: Q-Q plots of Duration (M5)
"' ' i ~ :. Ill' Ji 4i
-1
-2
qnorm
<.; ·~
<P'~
I
Figure 37: Q-Q plots of Bandwidth (M6)
10
~
i ~ 6
I i 4
·2
--'-~~~~~~~~~~
qnorm
,, sn
! "'
Figure 38: Q-Q plots of Median Time (M7)
~ ! 1·.:u.,:;>E!f\1(.;;.: .:; . ~
,.,-«''
.3 -2 -1 I) 1 2 3
.~' /
.,/ -3 ·2 -1 c- 1 2 3 .3 -2 -1 0 1 2 :J
I ! el Ji 3
ii
~
j ~ i ~ 1Q
ii '
·;·· ;:;·-:;;SIJKi "· ...
/' . ,.,..,,.,,
+i :;:;:i'!iF!~
,ff
,,_.,,/ -3 -2 -1 0 1 2 '3
qnorm
.3 ·2 ·1 0 1 2 3
; TM"ff';
./'/ •· ''.f\OJ<:.': · '"l' < :c<RUM :
,,__..-8 ,,,~-·
.J ·2 -1 0 1 2 3
qnorm
-3 -2 -1 0 1 2 3
,,•1 / QQ~ ')'.~"P<;. :~'.; J~.f1C';'' :;<'>--;-
~~'<.~'"' ,,___.,.,,-- ,/'
,_..../ -J -2 -1 0 1 2 J -J -2 -1 0 1 2 J
qnorm
-I
10
105
.3 ·2 ·1 0 1 2 l
" ·::-£-,./'.~ :S.-Qt<.e;:;_ ·!'<FW•:d• '{{>]:-·.:,: :·0.··JMP ,.
i 5 § u <J'
.j!
~ ii
! 10
/ .J ,____.,."f-o
~ i 4 / ~-,ti~K ~· .. ~·~''
~
,__/ ~ a
,oo_/j~ ,-"' "~
·2 .) -2 -1 0 1 2 3 .3 -2 -1 i) 1 2 J
qnorm qnoon
Figure 39: Q-Q plots of Temporal Concentration (M9)
20
15
0 ,.
~ §..
i 5
~ "' v ~
~
-10 ') ,)·'
~( <'> ,, ______ ../ r
-2
qnonn
0
1 ':i E 20
~ 15
~ 10
-5
-10
Figure 40: Q-Q plots of Temporal Asymmetry (MlO)
" { 'O a <ll ~
'"°"n.:•
'!{' . -~
•"
I LL
Ii ~ ::; .
i :i
..J -2 -1 0 1 2 3
ii>YiU'-.SOW ·., 1.·sot ;. :) ..:-:it:\:: .!::ndf!:' Y·'·,.
"' ___ .. ,,--I
:.:.u i:;imKin1r .• :hF"'··· -,.RCIF: · ·:: .i.• : ·:::m1W•:' ··
,,"'~~<;? <f~Q
-3 -2 -1 0 1 2 3 .J -2 ·1 -0 1 2
qnoon
..J ·2 ·I O 1 2 3
,,, '"'" :: '"'' ;;:,::so , ;~ l '.,_TMP~',';_ ··"
.:'"'
i
-~·: ,__.,,,-· ./·1,/ •"
•. ,.·<:i!lRttc>• · :·Roo: •. · .]7j;'. ·f'RDi.I
_ _,.../ ·•· I ___,,t••P
(-'>-~ 6~· .__...,.,.· ·2 -3 ·2 ·1 0 1 2 .3 -2 -1 0 1 2 3
qnorm qnorm
Figure 41: Q-Q plots of Median Frequency (Mll)
"' 15
10
.. -10
106
,/'<>c
ff'
;t
" .,.
i i ll
i g i:;'
l ,s ~ -'2
-4
·2
qnorm
·2
..
.J -2 ·1 0 l 2 3
:
/l.,-/.1/ ~"o
/I./·· -3 -2 -1 () 1 2 3
qnorm
r" /
.:19>'
•• <:i
-3 -2 _, 0 1 2 3
Figure 42: Q-Q plots of Frequency Asymmetry (M14)
.J ·2 ·1 0 1 2 3
""' p-,··
-2
-4
"' i ,_ ;!i =;
"' § ~ ~
/')""·' /,,~,
./ .. / i'i i a
~
§ i 2
m '5 !
I .;·~
li·~\W/>BRI¢>:: :n}.< :1 :'/llofii'i ,, ... <·RUM'.·· ..,,..,.
./ '·'
I ,, ,,;·
I • ,,,,.!
??
·I
·2 -3 -2 -1 "-0 1 2 3 .J ·2 -1 0 1 2 3
qnorm qnorm
Figure 43: Q-Q plots of Relative Time of Peak Overall Intensity (M18)
~ u. 15 .2
§ l1 ;'l
(}<~
·2
qnorm
l'l ~ ~ §
! ll J
.3 -1 ·1 0 1 2 3 J......,.J....,-- i l l
,, .·,:'.;sr. ·1r.tP~<(~
'
,___,,l/./~I /' .i·:·::.·:·aRK"''''' •·'l :.•ii .:1R0R· ·.:·r-- :1 :RUMY.
-·" ,~~"'?" I .,__,-· I . __.,,r· -J ·2 ·1 0 1 2 3 -J -2 -1 G 1 2 3
qnorm
Figure 44: Q-Q plots of Frequency of Peak Overall Intensity (M20)
-1
107
IO
"' "' t & (!'.
~· ': "' ' ~
"'
~·
.2
qnorm
Figure 45: Q-Q plots of AM Rate (M21)
2tl
15
I ~" i ~
oil
~------.r -2
qnorm
Figure 46: Q-Q plots of AM Rate Variation (M22)
12
10
"' ~
1 ti: 6
~ IS ~ 4 ,.
g
.. ____/ -2
qnorm
Figure 47: Q-Q plots of FM Rate (M23)
~
to ~
;:: :;;;
i (!'.
! 20 IS ~ ,5
.., 51 ~
10
~ 12
c.:i: ~o ,!j
.) •2 •t I} 1 2 J ! '
i______L__L '(, 'TMP
.J , ____ ,,r I .. _...r ,..'!llllt•• '•'!·-'.Ci!ibiL "'JjJJM.
,_
,__.,,,,.~·' I ,,__,,..,~.- ~~~;-:-.~
-3 -2 ·1 0 1 2 ) -J -2 -1 0 1 2 )
Qn<lf!TI
..J -2 -1 0 1 2 J '-'--L_L_'
0 !'SOti! +· -·TMP"' ·'"-'
.')!'.T!ii::!ll!!K;'.;!'{• .,•'.lt!T'71::;r-R0R •'· ., ·. "!'!/· . 'Rini!!" ·
, ,~
-3 -2 -I 0 1 2 3
_,,' ,---Ql1<Jf!TI
..J -2 -1 I) 1 2 3
J. ,,,,,,,,,.,...
-3 -2 -1 0 1 2 3
iK".TC0'1;E+ ;·;+: 'S• "'JMIFi'C. •;;
•'
_ ___; __ ___.,,,,,;:- ,'
,__,/ · -.,,J"iBRK' ·'' ·· i:'!':i'.! '.\ROR·· :C.';i,E".ii\' ('lIDM!f·
, ~..---""'"" ,,...-""'' I ·---··· .3 ·2 -1 0 1 2 J .) ·2 -1 (I 1 2 )
qnorm
-10
2tl
15
10
12
10
108
-3 -2 -1 0 1 2 J
+-<;/;: -SOK-• ~~P'.-
15
~ ~ 10
~
.. ~ ~
~ ·-s>''> .,..
i~~'.,:"tSPk'' "'' -"'-ROO:"'- ;-,.-----~"-----~ .. ;; . i 15
<I ;
-'" ~
-2
Cl'OfTTl
Figure 48: Q-Q plots of FM Rate Variation (M24)
•''
"' ~ /' g ~ 2
' i -r
{l
-2
qrorm
Figure 49: Q-Q plots of Overall Entropy (M26)
~ i ~
i i
i'-:>-'
,.p:
.. -2
<JlO!lTI
Figure 50: Q-Q plots of Upsweep Mean (M27)
"'
l :;, ~.
0---
~ ~
! i ~
-5
10
o~''C ,«¢.~- , . .,.,,-,,,,,,,.:
-3 -2 -1 0 I 2 3 -3 -2 -1 0 1 2 3
qoorm
..J -2 -1 0 1 2 J
0 I I ,~'l/'-/ ~~H?Sji;:•:· ----:"RORL: c-• f· --•;-.;,'ROM"•_..,_ -
,_/ ,~~ ,_,~-·
-J -2 -1 0 1 2 3 .3 -2 -1 0 1 2 3
qoorm
-l -2 -1 0 1 2 3
'''.U!:SllK''." i!_E,V ·::;·soc.,~::, ,_, .:' ~f'..;_ ~~~ ·>::ThlP·~~: :~<- !~·
.. --1 /.
,,/\~/ •:Uiit:'<aRKG?')::;v: ??t:ROR - f'' -----:q3!J~
~ ,•
-3 -2 -1 0 1 2 3
-~" ,,,,.,----
qno<m
~~,-
.3 -2 -t ti 1 2 3
" "
-5
109
~ 2
{ ~
i 0
ll
-2
).·.
•'
/
qnorm
Figure 51: Q-Q plots ofUpsweep Fraction (M28)
~ ~ £ ~ a <1l. ll
-2
-3 -2 -1 0 1 2 3
I "o',AAK ,A:<;:,~nn TMP'
/I./' / ,,, -2
>··liRil::·' '<1100' '''<•l -·-,-·< iliJlli;,·
/ j,/''" /,•' ..,,,,e
-3 -2 -1 1l 1 2 3 -3 -2 -~ 0 1 2 3
qnorm
110
Box PLOTS OF SCALED VARIABLES FOR IDENTIFYING OUTLIERS
0
ll -
0
II -
0
0 0 0
4 -0
0 0 0 0
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APPENDIX B: CLASSIFICITON TREE RAW DATA OUTPUT
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Figure 59: Classification tree final model - plots
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Table 12: Classification tree final model - raw data outout [l] 0.1220096 > surnmary(mod) Call: rpart(formula calltype - ., data params)
n= 836
CP nsplit rel error xerror xstd 1 0.27638191 0 1.0000000 1.0000000 0.02188309 2 0.22948074 1 0.7236181 0.7236181 0.02420221 3 0.16247906 2 0.4941374 0.4941374 0.02314367 4 0.05276382 3 0.3316583 0.3400335 0.02076694 5 0.03182580 5 0.2261307 0.2428811 0.01833773 6 0.01340034 6 0.1943049 0.2211055 0.01766021 7 0.01005025 7 0.1809045 0.2093802 0.01727080 8 0.01000000 8 0.1708543 0.2043551 0.01709825
Node number 1: 836 observations, complexity param=0.2763819 predicted class=SQL expected loss=0.7141148
class counts: 59 105 57 197 239 179 probabilities: 0.071 0.126 0.068 0.236 0.286 0.214
left son=2 (614 obs) right son=3 (222 obs) Primary splits:
LowerFreqM3 < 675.0855 to the left, improve=l63.3921, FMRateVarM24 < 0.0055 to the left, improve=l49.4871, MedianFreqMll < 561.782 to the left, improve=l00.2804, PkOverallFM20 < 530.3175 to the left, improve= 99.7010, DurationM5 < 0.4395 to the right, improve= 92.1828,
Surrogate splits:
(0 missing) (0 missing) (0 missing) (0 missing) (0 missing)
PkOverallFM20 < 1302.758 to the left, agree=O. 868, adj=0.505, (0 split) MedianFreqMll < 1318.912 to the left, agree=0.859, adj=0.468, DurationM5 < 2.4235 to the left, agree=0.787, adj=0.198, AMRateVarM22 < 0.007 to the right, agree=0.785, adj=0.189, TimeConcentM9 < 1.6775 to the left, agree=0.782, adj=0.180,
Node number 2: 614 observations, complexity param=0.2294807 predicted ciass=SQK expected loss=0.6840391
class counts: 59 105 57 194 29 170 probabilities: 0.096 0.171 0.093 0.316 0.047 0.277
left son=4 (452 obs) right son=5 (162 obs) Primary splits:
(0 split) (0 split) (0 split) (0 split)
FMRateVarM24 < 0.0055 to the right, improve=128.59100, (0 missing) AMRateVarM22 < 0.0215 to the right, improve=l20.66540, (0 missing) PkOverallFM20 < 530.3175 to the left, improve= 97.40044, (0 missing)
118
MedianFreqMll < 561.782 to the left, improve= 97.01046, (0 missing) BandwidthM6 < 2616.284 to the left, improve= 94.27497, (0 missing)
Surrogate splits: AMRateVarM22 < 0.0215 to the right, agree=0.879, adj=0.543, (0 split) DurationM5 < 0.287 to the right, agree=0.824, adj=0.333, (0 split) UpswpFracM28 < 34.3755 to the right, agree=0.800, adj=0.241, (0 split) AMRateM21 < 4.0295 to the left, agree=O. 796, adj=0.228, (0 split) TimeConcentM9 < 0.1265 to the right, agree=0.787, adj=0.191, (0 split)
Node number 3: 222 observations predicted class=SQL expected loss=0.05405405
class counts: 0 0 0 3 210 9 probabilities: 0.000 0.000 0.000 0.014 0.946 0.041
Node number 4: 452 observations, complexity param=0.1624791 predicted class=TMP expected loss=0.6238938
class counts: 58 105 57 33 29 170 probabilities: 0.128 0.232 0.126 0.073 0.064 0.376
left son=8 (237 obs) right son=9 (215 obs) Primary splits:
UpperFreqM4 < 1866.577 to the left, improve=91.40859, MedianFreqMll < 642.902 to the left, improve=89.47726, BandwidthM6 < 1361.89 to the left, improve=87.85287, PkOverallFM20 < 468.347 to the left, improve=85.20569, FMRateVarM24 < 0. 011 to the right, improve=77.18512,
Surrogate splits:
(0 missing) (0 missing) (0 missing) (0 missing) (0 missing)
BandwidthM6 < 1361.89 to the left, agree=0.985, adj=0.967, (0 split) MedianFreqMll < 680.2285 to the left, agree=0.889, adj=0.767, (0 split) EntropyM26 < 95.3805 to the left, agree=0.863, adj=0.712, (0 split) PkOverallFM20 < 468.347 to the left, agree=0.858, adj=0.702, (0 split) LowerFreqM3 < 241.5455 to the left, agree=O. 827, adj=0.637, (0 split)
Node number 5: 162 observations predicted class=SQK expected loss=0.00617284
class coilllts: 1 0 0 161 0 0 probabilities: 0.006 0.000 0.000 0.994 0.000 0.000
Node number 8: 237 observations, complexity param=0.05276382 predicted class=ROR expected loss=0.5738397
class counts: 46 101 56 19 11 4 probabilities: 0.194 0.426 0.236 0.080 0.046 0.017
left son=l6 (83 obs) right son=l7 (154 obs) Primary splits: ·
DurationM5 < 0.8675 MedianTimeM7 < 0.37 PkOverallFM20 < 170.0515 LowerFreqM3 < 384.906 AMRateVarM22 < 0.3255
Surrogate splits:
to the left, improve=33.89742, to the right, improve=28.32799, to the left, improve=23.31349, to the left, improve=22.73504, to the left, improve=22.52987,
(0 missing) (0 missing) (0 missing) (0 missing) (0 missing)
MedianTimeM7 < 0.4005 to the left, agree=0.920, adj=0.771, (0 split) AMRateM21 < 1.1595 to the right, agree=O. 869, adj=0.627, (0 split) FMRateM23 < 1.1525 to the right, agree=0.865, adj=0.614, (0 split) TimeConcentM9 < 0.2685 to the left, agree=0.857, adj=0.590, (0 split) UpperFreqM4 < 1184.326 to the right, agree=O. 7 85, adj=0.386, (0 split)
Node number 9: 215 observations, complexity param=0.01005025 predicted class=TMP expected loss=0.227907
class counts: 12 4 1 14 18 166 probabilities: 0.056 0.019 0.005 0.065 0.084 0.772
left son=18 (17 obs) right son=19 (198 obs) Primary splits:
UpswpFracM28 < 36.3945 FMRateVarM24 < 0.011 UpperFreqM4 < 3016.089 TimeConcentM9 < 1.318 BandwidthM6 < 2616.284
Surrogate splits:
to the left, improve=ll.40962, to the right, improve=ll.36771, to the left, i~prove=ll.33500,
to the right, improve=l0.79894, to the left, improve=l0.19102,
(0 missing) (0 missing) (0 missing) (0 missing) (0 missing)
TimeConcentM9 < 0.0305 to the left, agree=0.930, adj=0.118, (0 split)
119
TimeAsyrnmMlO DurationM5 FMRateM23
< -0 .2115 < 0.1325 < 15.4305
to the left, agree=O .930, adj=O .118, (0 split) to the left, agree=0.926, adj=0.059, (0 split) to the right, agree=0.926, adj=0.059, (0 split)
Node number 16: 83 observations, complexity param=0.0318258 predicted class=BRK expected loss=0.4939759
class counts: 42 11 0 19 9 2 probabilities: 0.506 0.133 0.000 0.229 0.108 0.024
left son=32 (55 obs) right son=33 (28 obs) Primary splits:
LowerFreqM3 < 325.017 PkOverallFM20 < 539.676 AMRateVarM22 < 0.0215 MedianFreqMll < 669.679 DurationM5 < 0.3615
Surrogate splits:
to the left, to the left, to the right, to the left, to the right,
improve=21.43904, (0 improve=l8.54354, (0 improve=l8.52093, (0 improve=l8.18897, (0 improve=13. 89051, ( O
missing) missing) missing) missing) missing)
MedianFreqMll < 662.2645 to the left, PkOverallFM20 < 539.676 to the left, DurationM5 < 0.3615 to the right, UpperFreqM4 < 1372.742 to the left, AMRateM21 < 2.7815 to the left,
agree=0.940, agree=0.940, agree=0.904, agree=0.867, agree=0.867,
adj=0.821, adj=0.821, adj=0.714, adj=0.607, adj=0.607,
(0 split) (0 split) (0 split) (0 split) (0 split)
Node number 17: 154 observations, complexity predicted class=ROR expected loss=0.4155844
class counts: 4 90 56 0 2 probabilities: 0.026 0.584 0.364 0.000 0.013
left son=34 (92 obs) right son=35 (62 obs) Primary splits:
param=0.05276382
2 0.013
PkOverallFM20 < 170.0515 to the right, improve=28.37610, (0 missing) AMRateVarM22 < 0.3255 to the left, improve=24.03463, (0 missing) FMRateVarM24 < 0.0695 to the left, improve=22.60592, (0 missing) LowerFreqM3 < 22.5425 to the right, improve=l8.17635, (0 missing) EntropyM26 < 25.117 to the right, improve=l5.06470, (0 missing)
Surrogate splits: MedianFreqMll < 181.817 to the right, agree=0.857, adj=0.645, (0 split) LowerFreqM3 < 35.6645 to the right, agree=O. 786, adj=0.468, (0 split) AMRateVarM22 < 0.087 to the left, agree=0.753, adj=0.387, (0 split) FMRateVarM24 < 0.0195 to the left, agree=O. 740, adj=0.355,. (0 split) UpperFreqM4 < 425.1125 to the right, agree=0.714, adj=0.290, (0 split)
Node number 18: 1.7 observations predicted class=SQK expected loss=0.4705882
class counts: 0 0 0 9 5 3 probabilities: 0.000 0.000 0.000 0.529 0.294 0.176
Node number 19: 198 observations predicted class=TMP expected loss=0.1767677
class counts: 12 4 1 5 13 probabilities: 0.061 0.020 0.005 0.025 0.066
Node number 32: 55 observations predicted class=BRK expected loss=0.2363636
class counts: 42 11 0 0 1 probabilities: 0.764 0.200 0.000 0.000 0.018
163 0.823
1 0.018
Node number 33: 28 observations, complexity param=0.01340034 predicted class=SQK expected loss=0.3214286
class counts: 0 0 0 19 8 1 probabilities: 0.000 0.000 0.000 0.679 0.286 0.036
left son=66 (19 obs) right son=67 (9 obs) Primary splits:
FMRateVarM24 AMRateVarM22 EntropyM26 BandwidthM6 PkOverallRel TM18
Surrogate splits:
< 0. 011 < 0.0215 < 97.3315 < 1012.061 < 56.2775
to the right, improve=ll.007940, to the left, improve= 9.385714, to the right, improve= 8.058442, to the right, improve= 4.919048, to the left, improve= 4.500000,
(0 missing) (0 missing) (0 missing) (0 missing) (0 missing)
AMRateVarM22 < 0.0215 to the left, agree=0.964, adj=0.889, (0 split)
120
EntropyM26 < 80.201 to the right, agree=0.929, PkOverallRelTM18 < 56.2775 to the left, agree=0.857, BandwidthM6 < 973.779 to the right, agree=0.821, MedianFreqMll < 693.84 to the right, agree=0.821,
Node number 34: 92 observations predicted class=ROR expected loss=0.1630435
class counts: 0 77 11 0 2 2 probabilities: 0.000 0.837 0.120 0.000 0.022 0.022
Node number 35: 62 observations predicted class=RUM expected loss=0.2741935
class counts: 4 13 45 0 0 0 probabilities: 0.065 0.210 0.726 0.000 0.000 0.000
Node number 66: 19 observations predicted class=SQK expected loss=O
class counts: 0 0 0 19 0 0 probabilities: 0.000 0.000 0.000 1.000 0.000 0.000
Node number 67: 9 observations predicted class=SQL expected loss=0.1111111
class counts: 0 0 0 0 8 1 probabilities: 0.000 0.000 0.000 0.000 0.889 0.111
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adj=0.778, (0 split) adj=0.556, (0 split) adj=0.444, ( 0 split) adj=0.444, (0 split)
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Figure 60: Classification tree validation model - plots
Table 13: Classification tree validation model - raw data output (1] 0.2368421 > summary(new) Call: rpart(formula calltype - ., data= parru~s)
n= 836
CP nsplit rel error xerror xstd 1 0.2763819 0 1.0000000 1.0000000 0.02188309 2 0.2294807 1 0.7236181 0.7252931 0.02420020 3 0.1624791 2 0.4941374 0.4974874 0.02317902 4 0.1000000 3 0.3316583 0.3400335 0.02076694
Node number 1: 836 observations, complexity param=0.2763819 predicted class=SQL expected loss=0.7141148
class counts: 59 105 57 197 239 179 probabilities: 0.071 0.126 0.068 0.236 0.286 0.214
left son=2 (614 obs) right son=3 (222 obs) Primary splits:
LowerFreqM3 < 675.0855 to the left, improve=163.3921, FMRateVarM24 < 0.0055 to the left, improve=149.4871, MedianFreqMll < 561.782 to the left, improve=l00.2804,
(0 missing) (0 missing) (0 missing)
121
PkOverallFM20 < 530.3175 to the left, improve= 99.7010, (0 missing) DurationM5 < 0.4395 to the right, improve= 92.1828, (0 missing)
Surrogate splits: PkOverallFM20 < 1302.758 MedianFreqMll < 1318.912 DurationM5 < 2.4235 AMRateVarM22 < 0.007 TimeConcentM9 < 1.6775
to the left, to the left, to the left, to the right, to the left,
agree=0.868, agree=0.859, agree=0.787, agree=0.785, agree=0.782,
adj=0.505, adj=0.468, adj=0.198, adj=0.189, adj=0.180,
Node number 2: 614 observations, complexity param=0.2294807 predicted class=SQK expected loss=0.6840391
class counts: 59 105 57 194 29 170 probabilities: 0.096 0.171 0.093 0.316 0.047 0.277
left son=4 (452 obs) right son=5 (162 obs) Primary splits:
(0 split) (0 split) (0 split) (0 split) (0 split)
FMRateVarM24 < 0.0055 AMRateVarM22 < 0.0215 PkOverallFM20 < 530.3175 MedianFreqMll < 561.782 BandwidthM6 < 2616.284
to the right, to the right, to the left, to the left, to the left,
improve=128.59100, improve=120.66540, improve= 97.40044, improve= 97.01046, improve= 94.27497,
(0 missing) (0 missing) (0 missing) (0 missing) (0 missing)
Surrogate splits: AMRateVarM22 < 0.0215 DurationM5 < 0.287 UpswpFracM28 < 34.3755 AMRateM21 < 4.0295 TimeConcentM9 < 0.1265
Node number 3: 222 observations
to the right, to the right, to the right, to the left, to the right,
agree=0.879, agree=0.824, agree=0.800, agree=0.796, agree=0.787,
predicted class=SQL expected loss=0.05405405 class counts: 0 0 0 3 210 9
probabilities: 0.000 0.000 0.000 0.014 0.946 0.041
adj=0.543, adj=0.333, adj=0.241, adj=0.228, adj=0.191,
Node number 4: 452 observations, complexity param=0.1624791 predicted class=TMP expected loss=0.6238938
class counts: 58 105 57 33 29 170 probabilities: 0.128 0.232 0.126 0.073 0.064 0.376
left son=8 (237 obs) right son=9 (215 obs) Primary splits:
(0 split) (0 split) (0 split) (0 split) (0 split)
UpperFreqM4 < 1866.577 to the left, improve=91.40859, (0 missing) MedianFreqMll < 642.902 to the left, improve=89.47726, (0 missing) BandwidthM6 < 1361. 89 to the left, improve=87.85287, (0 missing) PkOverallFM20 < 468.347 to the left, improve=85.20569, (0 missing) FMRateVarM24 < 0.011 to the right, improve=77.18512, (0 missing)
Surrogate splits: BandwidthM6 < 1361. 89 to the left, .agree=O. 985, adj=0.967, (0 split) MedianFreqMll < 680.2285 to the left, agree=0.889, adj=0.767, (0 split) EntropyM26 < 95.3805 to the left, agree=0.863, adj=0.712, (0 split) PkOverallFM20 < 468.347 to the left, agree=0.858, adj=0.702, (0 split) LowerFreqM3 < 241.5455 to the left, agree=0.827, adj=0.637, (0 split)
Node number 5: 162 observations predicted class=SQK expected loss=0.00617284
class counts: 1 0 0 161 0 0 probabilities: 0.006 0.000 0.000 0.994 0.000 0.000
Node number 8: 237 observations predicted class=ROR expected loss=0.5738397
class counts: 46 101 56 19 11 4 probabilities: 0.194 0.426 0.236 0.080 0.046 0.017
Node number 9: 215 observations predi~ted class=TMP expected loss=0.227907
class counts: 12, 4 1 14 18 166 probabilities: 0.056 0.019 0.005 0.065 0.084 0.772
122
APPENDIX C: PRINCIPAL COMPONENT ANALYSIS DATA - RAW DATA
OUTPUT
Table 14: PrinciEal ComEonent Anall'.sis - raw data outEut Standard deviation 2.1032890 1.8106105 1.31698516 1.15100919 1.11924231 Proportion of Variance 0.2460624 0.1823465 0.09647373 0.07368938 0.06967798 Cumulative Proportion 0.2460624 0.4284088 0.52488258 0.59857195 0.66824993
Loadings: Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
LowerFreqM3 -0.29 -0.14 -0.35 -0.17 0.14 UpperFreqM4 -0.42 0.25 0.15 DurationM5 -0.52 BandwidthM6 -0. 38 0. 35 0. 20 MedianTimeM7 -0.51 0.18 TimeConcentM9 -0.51 -0.11 0.12 TimeAsyrnmM10 -0.32 0. 35 MedianFreqMll -0.44 0.15 FreqAsyrnmM14 0.33 0.20 -0.23 PkOverallRelTM18 0.28 -0.47 PkOverallFM20 -0.38 -0.10 -0.24 -0.13 0.14 AMRateM21 0.24 -0.34 0.17 AMRateVarM22 0.23 0.28 0.12 0.42 FMRateM23 0.24 -0.25 0.10 FMRateVarM24 0.21 0.24 0.46 EntropyM26 -0. 38 0.24 0.15 UpswpMeanM27 0.17 -0.54 -0.29 UpswpFracM28 -0.16 0. 31 -0.48 -0.25
broken.stick(18) j E (j)
[1, l 1 0.194172671 [2' l 2 0.138617115 [3' l 3 0.110839338 [4, l 4 0.092320819 [5, l 5 0.078431930 [6, l 6 0.067320819 [7, l 7 0.058061560 [8, l 8 0.050125052 [9'} 9 0.043180608
[10,] 10 0.037007768 [11, l 11 0.031452212 [12,] 12 0.026401707 [13,] 13 0.021772078 [14,] 14 0.017498574 [15, l 15 0.013530320
'[16,] 16 0.009826616 [17,] 17 0.006354394 [18,J 18 0.003086420
Comp.l Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Proportion of Variance 0.2460624 0.1823465 0.09647373 0.07368938 0.06967798 0.05750879
123
APPENDIX D: ANALYSIS OF SIMILARITY (ANOSIM) DAT A OUTPUT
Table 15: Pair-wise ANOSIM plots for 15 pair-wise tests Pairs are noted above each plot -
BRKv.ROR BRKv.RUM
~ R:: 0??7 P:: <nntH R= n">7Q P- <nnn1
' ' ~ : : ;
or 0 §
~ §
s g
18 iii 0
~
0 0 ~
Between SRK ROR RUM SQK SOL TMP Between BRK ROR RUM SQK SOL TMP
BRKv.SQK BRKv.SQL 0
R=0~1<1 P=<•"'nn1 R::::fllAll P::<ilfl(l~
~
y '
§
8 g ... ~
~
i B 0
9 ~ §
0 ~ § + 0 0
Between BRK ROR RUM SQK SOL TMP Between BR~ ROR RUM SQK SQL TMP
BRKv.TMP RORv.RUM
D: ""'' P: <111101
~ ~= n.H7 P= <nt"\n1
g
~ ~
~ g 6 '
8 ~
0
§ ~ g j ~ ~
0 0 ~ i Between BRK ROR RUM SQK SQL TMP Between BRK ROR RUM SQK SQL TMP
124
RORv.SQK RORv.SQL
~
9 §
r l ~
~
s §
8 8 ~ l'l
~ ~ ~ 0
0 0
Between BRK ROR RUM SOK SOL TMP Bef'.Neen BRK ROR RUM SQK SQL TMP
RORv.TMP RUMv.SQK
~ ~ 1 g
t r Ii
T 0
! i
0
R ~
~ g
8 ·@
~ ~ -T
' § 0 0 t
Between BRK ROR RUM SQK SQL TMP Between BRK ROR RUM SQK SOL TMP
RUMv.SQL RUMv.TMP
O: n;:qs:.. P= c:"nn•
g
~ ~ 8
! ~ ~
~ ~ ~ 9 ~ r ~ t 0 0
Between BRK ROR RUM SQK SQL TMP Between BRK ROR RUM SQK SOL TMP
125
SQKv. SQL SQKv. TMP
i g - ____n~ 0
18 :::
i y g y H ;;\ :il i
f g
8 "' g
i 8 l ~ ~ 0
.'! -.-Between BRK ROR RUM SQK SQL TMP Between BRK ROR RUM SQK SOL 1MP
SQLv.TMP
!J 8 g~ ~
BetWeen BRK ROR RUM SQK SQL TMP
126
APPENDIX E: POWER SPECTRA OF RUMBLES AND NOISE
Rumble: 14 Hz to 1.2 Hz 105,--~~..--~~~~~--,~~~,.-~~-,.-~~----.
Rumble: call bandwidth 105~~~~~~~~~~~~~1
100
95
~ 00
~ ,! 851-
~ i;g" 00
75
70
v 65 200 400 600
frequency, Hz 800
Noise: 14 Hz to 1.2 kHz
100
95
v 90
85
1000 1200 80
100 200 3lO 400 500 600 700 800 frequency, Hz
Noise: 14 Hz to 20 kHz 95.-~~..--~~~~~--,~~~,.-~~-,.-~~----. 100~~..--~,.-~~~~~~~~~~~~..--~~~
70
65
ffi 200 400 600 800 ~ = frequency, Hz
~ ~
g ~
i ig"
90
80
30 2000 4000 GOOD 8000 10000 12000 14000 16000 18000 frequency, Hz
Figure 61: Power spectra of Rumble and background noise at the Oregon Zoo (example 1) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
127
Rumble: 14 Hz to 1.2 Hz 100
95~ n I~ 90 ~
~ . ~ a5
i " 80 rg"
75
70
65
90
~
~ 80 ~
i !g- 75
70
~ 100 200 300 400 500 600 700 800 900 1000 1100
frequency, Hz
Noise: 14 Hz to 1.2 kHz
65'--~~'--~~-'-~~--'-~~~'--~~-'-~~--' JJO 400 600 BOO 1000 1JJO
frequency, Hz
Rumble: call bandwidth 100
95
~ . 90 ~
! !g- 85
00
75'-~'--~--'-~~-'-~~-'-~~'--~--'-~~-'-__J 100 200 300 400 500 600 700
frequency. Hz
Noise: 14 Hz to 20 kHz 90~~~~~~~~~~~~~~~~~~~~~-
~ . 0 c ~
00
~ 60 §
rg" so
40
3)'-~-'-~-'-~-'-~-'-~--'-~~'-~'--~-'-~-'-~~ JJOO 4000 6000 8000 10000 12000 14000 16000 18000
frequency, Hz.
Figure 62: Power spectra of Rumble and background noise at the Oregon Zoo (example 2) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
128
~ ~ ~ 5 !fi
70
65
60
55 100
100
95
90
85
80
75
70
65
60
55i
Rumble: 14 Hz to 1.2 Hz Rumble: call bandwidth
Ell
~~
~ 75
70
200 300 400 500 600 700 800 65~~~~~~~~~~~~~~~~~-~~~~
OCIJ 1000 1100 5IJ M ~ D B m B a s 500 frequency, Hz frequency. Hz
Noise: 14 Hz to 1.2 kHz Noise: 14 Hz to 20 kHz 100~~~~~~~~~~~~~~~~~~~~
90
Ell
70
:m 400 500 800 1000 1200 20
2000 4000 6000 8000 10000 12000 14000 16000 1 BOOJ frequency, Hz frequency, Hz
Figure 63: Power spectra of Rumble and background noise at the Oregon Zoo (example 3) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
129
"" ~ u
65
~ 60
i § 55 aj
"" 50
45
40
Rumble: 14 Hz to 1.2 Hz
35 ~ 100 200 300 400 500 600 700 800 900. 1000 1100
frequency, Hz
Noise: 14 Hz to 1.2 kHz
65f
60f
55'
SOf
45
40
Rumble: call bandwidth
~\ ~
50 100 150 200 250 300 350 400 frequency.Hz
Noise: 14 Hz to 20 kHz 70~~~~~~~~~~~~~~~~~~~~ 10~~~~~--~~~~~~~~~~~~~~
65
-g 55 g
j 50
rg" 45
40
35
3J 200 400 600 frequency, Hz
800 1000 1200
65
60
55
"" g 50
i 45 § !g- 40
25
20 2000 4000 6DOO 8000 10000 12000 14000 16000 18000 frequency, Hz
Figure 64: Power spectra of Rumble and background noise at the Elephant Nature Park (example 1) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
130
Rumble: 14 Hz to 1.2 Hz Rumble: call bandwidth oo~~~~~~~~~~~~~~~~~~~~~ so~~~~~~~~~~~~~~~~~~~~~
75
70
65
~
~ 60 ~ i 55 § !fl. 50
45
40
35
30 200 400 ---
frequency, Hz
Noise: 14 Hz to 1.2 kHz
65
60
55
50
45 40 60 BO 100 120 140 160 180 200 220
frequency, Hz
Noise: 14 Hz to 20 kHz oo~~~~~~~~~~~~~~~~~~~---, oo~~~~~~~~~~~~~~~~~~~---,
75
70
65
45
40
35 ~:
30'---'-~-'-~-'----''-----'~-'-~-'-~-'--~'-----'~--'--~~ 100 200 300 400 500 600 700 800 900 1000 1100
frequency, Hz
70
60
50
10 ~ ~ = ~1~1=1~1~1~ frequency. Hz
Figure 65: Power spectra of Rumble and background noise at the Elephant Nature Park (example 2) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
131
Rumble: 14 Hz to 1.2 Hz BO
75
70
65
... g 60 ~
j 55
~· 51)
45
40
35
30 100 200 300 400 500 600 700 800 900 100J 1100
frequency, Hz
Noise: 14 Hz to 1.2 kHz 70.----~~~~~~~~~~~~~~.----~~~~
65
~ e so
i 45
iii 40
35
30
25 100 200 300 400 500 600 700 800 900 1000 1100
frequency, Hz
Rumble: call bandwidth so~~~~~~~~~~~~~~~~~~~~~~
] 65
~ 60
~ ~· 55
50
45
40 50 100 150 200 250 300
frequency, Hz
Noise: 14 Hz to 20 kHz 70.----~~~~~~~~~~~~~~~~~~~
iii
60
50
10~~~~~~~~~~~~~~~~~~~~~~
2000 4000 6000 8000 10000 12000 14000 1600) 18000 frequency, Hz
Figure 66: Power spectra of Rumble and background noise at the Elephant Nature Park (example 3) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
132
Rumble: 14 Hz to 1.2 Hz Rumble: call bandwidth Ill Ill
75 75
70
65 70
~ ~
~ 60 ~
w g 65 ~
i 55
; ro i § 60 !ll.
45 55
40 50
35
~ 100 200 300 400 500 600 700 800 900 1000 11 DO
45 50 100 150 200 250 300 350
frequency, Hz frequency, Hz
Noise: 14 Hz to 1.2 kHz Noise: 14 Hz to 20 kHz Ill.-~~.-~~~~~~~~~~~~~~~--, BO.-~.-~.-~.-~.-~.-~.-~.-~.-~.---,
75
70
~~~--'~~~~~~-'-~~~~~---'-~~--'
200 400 600 frequency, Hz
800 1000 1200
70
60
10 =~ ~~1=1=1~1~1~ frequency, Hz
Figure 67: Power spectra of Rumble and background noise at the Elephant Nature Park (example 4) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
133
.,, 65
i i SJ
~.55
50
45
Rumble: 14 Hz to 1.2 Hz
.,, . ~ 65
i " 60 ill-
55
50
Rumble: call bandwidth
40 100 200 300 400 500 600 700 800 9()0 1000 1100
frequency, Hz
45L-LC.---'-.---'-.--~.---'-.---'-.---L.----'.--.--L-.---'-J 50 100 150 200 250 300 350 400 450 500
frequency, Hz
Noise: 14 Hz to 1.2 kHz Noise: 14 Hz to 20 kHz 80.--.--.--.--.--.--~.--.--~.--.--.--~.--.--~.--.-----. 80.--.--~.--~.--~.--~.--~.--~.--~.--~.--~~
'g 65 ~ I!! -s 60 § ~- 55
50
45
40 200 400 600 800 1000 1200
frequency, Hz
.,, .
70
60
~ 50
i 40 ill-
30
20
10 2000 4000 6000 8000 10000 12000 14000 16000 18000
frequency, Hz
Figure 68: Power spectra of Rumble and background noise at the Royal Elephant Kraal (example 1) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
134
Rumble: 14 Hz to 1.2 Hz Rumble: call bandwidth 85.---~~~~~~~~~~~~~~~~~~~~
\f\~ 65
60
55
50
45
1000 1200 Ellll 400 600 35
200 40 -
200 300 400 500 600 10U frequency, Hz frequency, Hz
Nmse. . . 14 Hz to 1.2 kHz Noise: 14 Hz to 20 kHz 70 m~~~~~~~~~~~~~~~~~~~~~
~ 55 e i 50 5 !g. 45
40
35
30 100 200 DJ 400 500 600 700 Ellll 900 1000 1100
frequency, Hz
~ ~ u
60
50
~ i 40
~ 30
20
w ~ ~ =~1~1=uooo1=1~ 1requem:y, Hz
Figure 69: Power spectra of Rumble and background noise at the Royal Elephant Kraal (example 2) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
135
Rumble: 14 Hz to 1.2 Hz Rumble: call bandwidth 85~~~~~~~~~~~~~~-....~~~~-.... as~-....~~~~~~~~~~~~~~~~~-....
00
.,, g 65 !!
~ ro § !g. 55
50
45
40
35 100 200 300 400 500 600 700 800 900 1000 1100
frequency, Hz
Noise: 14 Hz to 1.2 kHz
60
55
50
45
40
35 100
~ I./
200 300 400 500 600 700 800 900 frequency, Hz
Noise: 14 Hz to 20 kHz 65~·~~~~~~~~~~~~~~~~~~
i: 50
~ 'i 45 §
~ 40
35
30
1200 25'-----x~~~o;!;n-----'--_J 800 11XXJ 200 400 600 frequency, Hz
60
55
50
45
20 21XXJ 4000 6000 8000 10000 12000 14000 16000 18000
frequency, Hz
Figure 70: Power spectra of Rumble and background noise at the Royal Elephant Kraal (example 3) Frequency is on the x-axis. Power (dB) is on the y-axis. Power is referenced to the same value.
136