tracking beat tracking beat Mcgill university :: music technology :: mumt 611>>

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  • tracking beat tracking beat Mcgill university :: music technology :: mumt 611>>
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  • tracking beat WHAT IS BEAT TRACKING? /17 0
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  • tracking beat What is beat tracking? magic box Input audio output tatum locations /17 1
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  • tracking beat What is beat tracking? Estima(tion) of the possibly time-varying tempo and locations of each beat. In Engineering terms, this is the frequency and Phase of a time-varying signal, the phase Of Which is zero at a beat location (I.e., where One would tap ones foot). Hainsworth, 2006 /17 2
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  • tracking beat Overview Motivation & definitions early work Discrete vs. continuous detection functions canonical work: scheirer autocorrelation versus comb filtering Exemplar Methods Persistent problems >> /17 3
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  • tracking beat Motivation & definitions Auto accompaniment Synchronization of 2 streams Cd skipping recovery Time-scaling algorithms Tempo-synchronous effects/control Database retrieval similarity >> /17 4
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  • tracking beat Motivation & definitions Blimes divisions of musical timing metrical structure tempo variation timing deviations arrhythmic sections 3 hierarchal levels of metrical structure Tempo tactus tatum Beat tracking / Tempo induction >> /17 5
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  • tracking beat Approach overview Rule based steedman (1977) parncutt (1994) Autocorrelation Brown (1993) *Davies & Plumbley (2005) Oscillating filters *Large (1994) *Scheirer (1998) histogramming *seppanen (2001) Multiple agent *goto (1995) Dixon (2001) probabilistic hainsworth & macleod (2003) *klapuri (2003) >> Red audio Black symbolic * causal = = = /17 6
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  • tracking beat Early Work >> music perception and comp sci (1980 s ) Most early work with midi/symbolic data Rule based /17 7
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  • tracking beat Early Work steedman (1977) large (1994) Goto (1995) Scheirer (1998) >> MIDIMIDI AUDIOAUDIO...... /17 8
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  • tracking beat Discrete df vs. continuous df Discrete detection function localized onset points, or IOI (inter-onset intervals) Suited for monophonic signals step 1: Created by various comparative time or time-freq techniques step 2: peak picking technique Continuous detection function better for unknown onset densities same as step 1 above further processing required for important results >> /17 9
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  • tracking beat Scheirer Input audio Filt_1 Filt_2 Filt_3 Filt_4 Filt_5 Filt_6 Env_1 frequency filterbank Continuous enveloping...... Comb filterbank......... Sum fltbks Peak pick /17 10
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  • tracking beat ACF vs Comb filt Automatic phase alignment Possible tempi at multi & fracs Meter estimation directly avail Phase Alignment in 2 nd step Commonality not given directly Meter estimation via decim. & sum Less expensive >>. Comb filters * >> /17 11
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  • tracking beat goto Input audio Dscrt Onset det Acf period Prior knowledge: 1) frequent ioi is likely ibi 2) sounds likely to occur on beats 3) rhythmic pattern templates 4) chord templates for non-perc music Prior kn0w Sub_1 Sub_2 Sub_3 Sub_7 freq fltrbk...... Cross corr align Multi agents winner /17 12
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  • tracking beat klapuri Input audio Filt_01 Filt_02 Filt_03 Filt_36 freq fltrbk...... Chan_1 Chan_2 Chan_3 Chan_4...... Comb fltrbk...... norm Pwr env Period & Align estim Bar beat tatum S n-1 Bar beat tatum SnSn HMM P(s n q n ) | Observable variable conditioned by current state = Train data Training data rhythmic pattern templates = /17 13
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  • tracking beat Davies & plumbley Input audio Cont Detect func acf periodicityalignment Cont dep state Gen state 2 state model...... Comb fltrbk............ Comb fltrbk...... /17 14
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  • tracking beat comparison Davies & plumbley klapuri scheirer 55.961.471.280.9 23.838.929.848.5 54.861.268.178.9 raw allowed Cml%Tot%Cml%Tot% Raw Cml correct metrical level, continuity required raw tot correct metrical level, continuity not required Allowed cml 1/2 & 2x tempo allowed, continuity required Allowed cml 1/2 & 2x tempo allowed, continuity not required = = = = /17 15
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  • tracking beat Persistent problems Periodicity switching Half/double time Alignment issues Expressive timing Non-percussive music Areas for future work >> /17 16
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  • tracking beat conclusions much progress has been made through several approaches Possible New methods of extracting periodicity and phase we need to work on improving the robustness of calculations Thank you for your time! >> /17 17
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  • tracking beat references Davies, M.E.P., M. Plumbley. Context-dependent beat tracking of musical Audio, IEEE Transactions on Audio, Speech and Language Processing, 15(3), 2007, pp. 1009-20. Goto, M. A study of real-time beat tracking for musical audio signals. PhD thesis, waseda university, 1998. Hainsworth, s.w. beat tracking and musical metre analysis, in Signal processing methods for music transcription, edited by a. Klapuri, and M. Davy, 101-129. New york: Springer science and business media, 2006 Hainsworth, s.w. techniques for the automated analysis of musical audio, PhD thesis, department of engineering, university of cambridge, 2004. KLAPURI, A. SIGNAL PROCESSING METHODS FOR THE AUTOMATIC TRANSCRIPTION OF MUSIC PHD THESIS, TAMPERE UNIVERSITY OF TECHNOLOGY, 2004. Scheirer, e. music listening systems, PhD thesis Massachusetts institute of Technology, 2000.

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