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 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
Thanksemail@example.com @gamboviolhttp://mir-in-action.blogspot.comhttp://playground.last.fm/demo/speedohttp://users.last.fm/~mark/speedo.tgzWe are looking for interns/research fellows!
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