Crowd sourcing for tempo estimation
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Crowd sourcing for tempo estimation



Slides for presentation at ISMIR 2011 of the paper "Improving perceptual tempo estimation with crowd-source annotations".

Slides for presentation at ISMIR 2011 of the paper "Improving perceptual tempo estimation with crowd-source annotations".



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Crowd sourcing for tempo estimation Crowd sourcing for tempo estimation Presentation Transcript

  • Improving perceptual tempo estimation with crowd-sourced annotations Mark Levy, 26 October 2011
  • Tempo EstimationTerminology: tempo = beats per minute = bpm
  • Tempo EstimationUse crowd-sourcing: quantify influence of metrical ambiguity on tempo perception improve evaluation improve algorithms
  • Perceived TempoMetrical ambiguity: listeners don’t agree about bpm typically in two camps perceived values differ by factor of 2 or 3McKinney and Moelants: 24-40 subjects released experimental data
  • Perceived Tempo Metrical ambiguity:listeners listeners bpm bpm McKinney and Moelants, 2004
  • Machine-Estimated TempoAlso affected by metrical ambiguity: makes estimation difficult natural to see multiple bpm values estimated values often out by factor of 2 or 3 (“octave error”)
  • Crowd SourcingWeb-based questionnaire: capture label choices capture bpm from mean tapping interval capture comparative judgements
  • Crowd Sourcing
  • Crowd Sourcing Music:  over 4000 songs  30-second clips• rock, country, pop, soul, funk and rnb, jazz, latin, reggae, disco, rap, punk, electronic, trance, industrial, house, folk, ...• recent releases back to 60s
  • ResponseFirst week (reported/released): 4k tracks annotated by 2k listeners 20k labels and bpm estimatesTo date: 6k tracks annotated by 27k listeners 200k labels and bpm estimates
  • Analysis: ambiguityWhen people tap to a song at different bpm do they really disagree about whether it’s slow or fast?Investigation: inspect labels from people who tap differently quantify disagreement for ambiguous songs
  • Analysis: ambiguitySubset of slow/fast songs: labelled by at least five listeners majority label “slow” or “fast”
  • Analysis: ambiguitybpm vs speed labelall estimates for slow/fast songs
  • Analysis: ambiguitybpm vs speed label people can tap slowly to fast songsall estimates for slow/fast songs
  • Analysis: ambiguityLabels for fast songs from slow-tappers
  • Analysis: ambiguityQuantify disagreement over labels: model conflict, extremity of tempo conflict coefficient min(Ls , L f ) Ls Lf C max(Ls , L f ) L Ls, Lf, L: number of slow, fast, all labels for a song
  • Analysis: ambiguityDistribution of conflict coefficient C C > 0 means slow and fastall songs with at least five labels
  • Analysis: ambiguitySubset of metrically ambiguous songs: at least 30% of listeners tap at half/twice the majority estimateCompared to the rest: no significant difference in C
  • Evaluation metricsMIREX: capture metrical ambiguity replicate human disagreementAmbiguity considered unhelpful: automatic playlisting DJ tools, production tools jogging
  • Evaluation metricsApplication-oriented : compare with majority* human estimate (*median in most popular bin) categorise machine estimates  same as humans  twice as fast  twice as slow  three times as fast  and so on  unrelated to humans
  • Analysis: evaluationSources: BPM List (DJ kit, human-moderated) Donny Brusca, 7th edition, 2011 EchoNest/MSD (closed-source algorithm) maybe Jehan et al,? VAMP (open-source algorithm) Davies and Landone, 2007-
  • Analysis: machine vs human 80% 70% 60% 50% BPM List 40% VAMP 30% EchoNest 20% 10% 0% x2 same /2 unrelated other
  • Analysis: controlled testControlled comparison: exploit experience from website A/B testing use this to improve algorithm iterativelyResult is independent of any quality metric
  • Analysis: controlled testWhen visitor arrives at the page: choose a source S at random choose a bpm value at random choose two songs given that value by S display them togetherThen ask which sounds faster!
  • Analysis: controlled testNull Hypothesis: there will be presentation effects listeners will attend to subtle differencesbut these effects are independent of the source of bpm estimates if the quality of the sources is the same
  • Analysis: controlled test 100% 90% 80% 70% 60% 50% different 40% same 30% 20% 10% 0% BPM List VAMP EchoNest
  • Analysis: improving estimatesAdjust bpm based on class: imagine an accurate slow/fast classifier Hockmann and Fujinaga, 2010 adjust as follows: bpm:= bpm/2 if slow and bpm > 100 bpm:= bpm*2 if fast and bpm < 100 otherwise don’t adjust simulation: accept majority human label
  • Analysis: adjusted vs human 80% 70% 60% 50% BPM List 40% VAMP 30% EchoNest 20% 10% 0% x2 same /2 unrelated other
  • ConclusionsCrowd sourcing: gather thousands of data points in a few days, half a million over time humans agree over slow/fast labels, even when they tap at different bpmImproving machine estimates: use controlled testing exploit a slow/fast classifier
  • Thanks! @gamboviolhttp://mir-in-action.blogspot.com are looking for interns/research fellows!