how do they know that? exploring processing in digital ... · 59 adaptive beamforming...

Nikolas Klakow, AuD

Customer Trainer, Phonak LLC

How do they know that?

Exploring Processing in Digital Hearing Aids

2

Agenda

Introduction

Sound Classification

Noise Reduction

Directional Microphones

3

How does the brain know what this is?

4

How we look at sounds

Time-domain graph

Spectrum

Spectogram

5

Fast Fourier Transform

The FFT is an algorithm for computation of discreet Fourier transform (DFT).

Allows a system to work in the frequency domain

Basic calculation that will be used for:

Signal amplification

Feedback cancellation

Noise reduction

Adaptive directionality

Spectral enhancement/reduction

6

Frequency Analysis

Compound Signal

7

/eh/

8

Frequency Analysis - 2

9

The vowels /eh/ /a/

/e/ /oh/

10

Trivia

When was the Fast Fourier Transform discovered?

1866

1805

1918

1956

German Mathematician Carl Friedrich Gauss credited with discovery

11

Agenda

Introduction


Noise Reduction


12


Speech

Music

Noise

13

What is that?

14


Periodicity

Spectral Envelope/Prediction

Statistical Evaluation

15

Periodicity

Periodic sounds:

Music generally periodic

Vowels

Some consonant i.e. /n/

Aperiodic sounds

Sibilants

Noise

Schaub, A. Digital Hearing Aids, 2008

16

Autocorrelation

How does short signal segment match previous one?

Coefficient quantifies correspondence between segments with values between +1 to -1

What is the period of example (a)?


8.5ms

17

Autocorrelation Coefficient

Value close to +1

indicates matching segments

Value around 0

indicates completely different segments

Value close to -1

indicates identical waveforms, but opposite phase

/n/


18

What is that?

19

Autocorrelation of samples

20

AC for Speech

Y-axis in Figure (b) shows delay between signal segments.

Period increases gradually from 9 to 10ms

Corresponds to fundamental freq. from ~110 to 100Hz changing slowly: speech trait


21

AC for Pulsed Noise

Noise from dishes in sink.

Waveform alone has modulation like speech

Lack of correlation suggest signal is not speech


22

How about this?

23

Autocorrelation of samples - 2

24

AC for Music

Easy Listening music for flute and guitar.

Continuous periodicity allows system to differentiate between noise, even if signal has little modulation.


25

AC for Steady Noise

Minimal modulation

High AC (0.9), but period change irregularly between 10 and 16ms

Corresponds to fast changes of fundamental freq. from 60 to 100Hz

Typical of low freq noise, not speech.


26


Periodicity



27

Spectral Envelope

Greater precision with higher order envelopes

Predictability becomes a parameter


Spectrum

16th order envelope

2nd order envelope

1st order envelope

/s/

/ae/

28

Normalized Prediction Error

Nearer to 1 – flatter spectrum

Nearer to 0 – more pronounced resonances


Music

29

Normalized Prediction Error - 2


Music Steady Noise

30

Putting clues together

31


Periodicity



32


Using statistical analysis systems can determine likelihood of sounds being music, noise, or speech based on attributes:

Periodicity, fundamental frequency, spectrum gradient, dominant resonance

33


Speech

Music

Noise

34

Agenda

Introduction


Noise Reduction


35

Noise Reduction

Speech and noise – what to do?

36

Block diagram of NR bands

Band-pass filter

High-pass filter

Low-pass filter

Determine attenuation



37

Divide and process

0-500 Hz

500-2000 Hz

2-8 kHz

38

Maxima and minima

Measure max/min modulation in discreet bands

Maxima

Minima

39

Modulation

Subtract minima from maxima


40

ANSI S3.5-1997: The SII

Speech Intelligibility Index

Between 0 and 1

Indicates proportion of speech cues available

Conditions to reach “1”

1. Level of speech signal ≤ 10dB above normal speech at distance of 1 meter

2. Speech signal must exceed noise by 15+dB

41

The 15dB rule

Modulation > 15dB

no attenuation

Modulation < 15dB

attenuated by difference between 15dB and own modulation

Modulation (dB)

0 0

5

5 10

10

15

15

Att

enuation (

dB)

42

Modulation

Subtract minima from maxima


43

Attenuation values

Varying attenuation in bands


44

Bands after attenuation


45

And the difference…

Before NR

After NR


46

Thoughts on NR

Should it be SPL dependent?

How does it interact with WDRC?

47

More Trivia

The Fourier Transform was named in honor of the French mathematician, Joseph Fourier (1768-1830)

He is credited with the discovery of the Greenhouse effect.

48

Agenda

Introduction


Noise Reduction


49

The Directional Pattern

Front

Back

1.Cardioid

2.Hypercardioid

3.Supercardioid

4.Dipole

Null = azimuth at which there is the least amplification

50

Dual Omnidirectional Microphones

Benefits:

Adaptive polar patterns

Multiple polar patterns

d

T

xF(t)

xR(t-T)

Difference signal

d(t) Front

Rear

51

Directionality Basics

Speed of sound : 340 m/s (c)

Microphone separation (d) : 8.5 mm

Time delay between microphones = 25 μs

Electronic Delay (T): 25 μs

Cardioid pattern – cancellation at 180°

d

T

xF(t)

xR(t-T)

Difference signal

d(t) Front

Rear

-

+

52

Math Alert!

8.5 mm = 0.0085 m (d)

Speed of Sound 340 m/s (c)

0.0085 m : 340 m/s = 0.000025 s

0.000025 s = 25μs

d/c provides the time it takes the sound to

get from one microphone to the other.

53

Sound from Behind

Hypothetical calculation:

Sound enters rear mic with value 100

Sound enters front mic with value of 75 (25 delay)

Electronic delay T= 25

Therefore : (xF(t) = 75) – (xR(t-T) = 75) = 0

d

T

xF(t)

xR(t-T)

Difference signal

d(t) Front

Rear

Sound

+

-

54

Sound from Front

Hypothetical calculation:

Sound enters front mic with value 100

Sound enters rear mic with value of 75 (25 delay)

T= 25

Therefore : (xF(t) = 100) – (xR(t-T) = 50) = 50

d

T

xF(t)

xR(t-T)

Difference signal

d(t) Front

Rear

Sound

+

-

55

Demonstration Signal

4000Hz

2000Hz

1000Hz

500Hz Compound Waveform

Spectrum

56

Sound from the Front (75dB)

a) Microphone signals

b) Difference signal


Output signal spectra

57

Sound from the Side (75dB)

Output signal spectra

a) Microphone signals

b) Difference signal


58

Adapting Directional Pattern Nulls

Without changing T, polar pattern could not redirect null for maximum reduction of signal

59

Adaptive Beamforming

Green-difference signal front-cardioid pattern dF(t)

Yellow-difference signal rear-cardioid pattern dR(t)

Blue-variable directional pattern output signal y(t)

Red-calculates microphone gain (b) defining null direction


d

xF(t)

xR(t)

dF(t) Front

Rear dR(t)

d/c

d/c Self-

Adjustment

y(t) +

-

+

+

-

-

b

60

Variable Directional Patterns

Equation:

y(t) = dF(t) – (b · dR(t))

b = 1 b = 0.5


61

Self-Adjusting Directionality

Speech sample used for following examples


62

Self-Adjusting Directionality - 2

Difference signals of Speech in

Noise-Cardioid pattern.

(a) Front dF(t)

(b) Rear dR(t)


63

Directional patterns and output signals for varying values of factor b


b = 0.13

b = 0.53


64

This b value reduces enough noise to allow stop consonants /t/ and /d/ to be audible.


b = 0.33


Original

65

Questions?

Kates, J.M. Digital Hearing Aids, Plural Publishing, 2008

Schaub, A. Digital Hearing Aids, Thieme Publishing, 2008

Dillon, H. Hearing Aids, Thieme Publishing, 2001

how do they know that? exploring processing in digital ... · 59 adaptive beamforming...

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