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tracking

Mcgill university :: music technology :: mumt 611 >> beat tracking … WHAT IS BEAT TRACKING? 0 /17 What is beat tracking? Input audio … magic box … output tatum locations 1 /17 What is beat tracking? “…Estima(tion) of the possibly time-varying

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tracking

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

  2. … WHAT IS BEAT TRACKING? 0 /17

  3. What is beat tracking? Input audio … magic box … output tatum locations 1 /17

  4. 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 one’s foot).” Hainsworth, 2006 2 /17

  5. Overview >> Motivation & definitions … early work … Discrete vs. continuous detection functions … canonical work: scheirer … autocorrelation versus comb filtering … Exemplar Methods … Persistent problems … >> >> >> >> >> >> 3 /17

  6. Motivation & definitions >> Auto accompaniment Synchronization of 2 streams Cd skipping recovery Time-scaling algorithms Tempo-synchronous effects/control Database retrieval similarity >> >> >> >> >> >> 4 /17

  7. 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 >> >> >> 5 /17

  8. 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 = = 6 /17 =

  9. Early Work >> music perception and comp sci (1980’s) Most early work with midi/symbolic data Rule based >> >> 7 /17

  10. Early Work >> steedman (1977) large (1994) Goto (1995) Scheirer (1998) M I D I >> >> A U D I O >> . . . >> 8 /17

  11. 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 >> 9 /17

  12. Scheirer . Comb filterbank frequency filterbank Continuous enveloping . . Filt_1 Env_1 . . . Filt_2 . Filt_3 . . . . . . Input audio Sum fltbks . . . . Filt_4 . . . . Filt_5 . . . Filt_6 Peak pick 10 /17

  13. ACF vs Comb filt . Comb filters * Phase Alignment in 2nd step Commonality not given directly Meter estimation via decim. & sum Less expensive Automatic phase alignment Possible tempi at multi & fracs Meter estimation directly avail >> >> >> >> >> >> >> 11 /17

  14. winner goto freq fltrbk Multi agents Sub_1 Input audio period align Sub_2 Dscrt Onset det Cross corr Sub_3 Acf . . . Prior kn0w Sub_7 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 12 /17

  15. HMM Bar Bar beat beat tatum tatum Sn-1 Sn | P(sn qn) Observable variable conditioned by current state = klapuri . Period & Align estim freq fltrbk norm Pwr env . . Comb fltrbk Filt_01 Chan_1 Input audio Filt_02 . Chan_2 . . . . Filt_03 . Chan_3 . . . Train data Chan_4 Filt_36 Training data rhythmic pattern templates = 13 /17

  16. Davies & plumbley . . . . periodicity alignment . . Comb fltrbk Comb fltrbk . . Cont Detect func . . Input audio . . . . . . acf . . Cont dep state Gen state 2 state model 14 /17

  17. comparison allowed raw Cml% Tot% Cml% Tot% 23.8 38.9 29.8 48.5 scheirer 55.9 61.4 71.2 80.9 klapuri Davies & plumbley 54.8 61.2 68.1 78.9 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 = = = = 15 /17

  18. Persistent problems Areas for future work >> Periodicity switching Half/double time Alignment issues Expressive timing Non-percussive music >> >> >> >> 16 /17

  19. 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

  20. 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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