Data Driven Music Understanding
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1 ata riven Music Understanding an Ellis Laboratory for Recognition and Organization of Speech and udio ept. Electrical Engineering, olumbia University, NY US 1. Motivation: What is Music? 2. Eigenrhythms 3. Melodic-Harmonic Fragments 4. Example pplications ata-riven Music Understanding - an Ellis /31
2 LabROS : Machine Listening Extracting useful information from sound escribe utomatic Narration Emotion Music Recommendation lassify Environment wareness SR Music Transcription Sound Intelligence V Speech/Music Environmental Sound Speech ectect Task... like (we) animals do ata-riven Music Understanding - an Ellis Music omain /31
3 1. Motivation: What is music? What does music evoke in a listener s mind?? Which are the things that we call music? ata-riven Music Understanding - an Ellis /31
4 Oodles of Music Bertin-Mahieux et al. 09 What can you do with a million tracks? ata-riven Music Understanding - an Ellis /31
5 Re-use in Music Serrà et al What are the most popular chord progressions in pop music? ata-riven Music Understanding - an Ellis /31
6 Potential pplications ompression lassification Manipulation ata-riven Music Understanding - an Ellis /31
7 2. Eigenrhythms: rum Track Structure To first order, all pop music has the same beat: Ellis & rroyo 04 an we learn this from examples? ata-riven Music Understanding - an Ellis /31
8 Basis Sets ombine a few basic patterns to make a larger dataset data weights X = W H patterns 1 = 0-1 ata-riven Music Understanding - an Ellis /31
9 rum Pattern ata Tempo normalization + downbeat alignment ata-riven Music Understanding - an Ellis /31
10 NMF Eigenrhythms Posirhythm 1 Posirhythm 2 HH HH SN SN B B Posirhythm 3 Posirhythm 4 HH HH SN SN B B Posirhythm 5 Posirhythm 6 HH HH SN SN B B samples (@ beats (@ 120 Nonnegative: only add beat-weight ata-riven Music Understanding - an Ellis /31
11 Eigenrhythm BeatBox Resynthesize rhythms from eigen-space ata-riven Music Understanding - an Ellis /31
12 3. Melodic-Harmonic Fragments How similar are two pieces? an we find all the pop-music clichés? ata-riven Music Understanding - an Ellis /31
13 MF Features Used in speech recognition Let It Be (LIB-1) - log-freq specgram freq / Hz MFs oefficient Noise excited MF resynthesis (LIB-2) freq / Hz time / sec ata-riven Music Understanding - an Ellis /31
14 hroma Features Idea: Project onto 12 semitones regardless of octave Fujishima 1999 chroma F freq / khz chroma F fft bin time / sec time / frame M X = mapping signal chroma ata-riven Music Understanding - an Ellis /31
15 hroma Features To capture musical content Let It Be - log-freq specgram (LIB-1) freq / Hz chroma bin B E hroma features Shepard tone resynthesis of chroma (LIB-3) freq / Hz MF-filtered shepard tones (LIB-4) freq / Hz time / sec ata-riven Music Understanding - an Ellis /31
16 Beat-Synchronous hroma ompact representation of harmonies Let It Be - log-freq specgram (LIB-1) freq / Hz Onset envelope + beat times chroma bin B E Beat-synchronous chroma Beat-synchronous chroma + Shepard resynthesis (LIB-6) freq / Hz time / sec ata-riven Music Understanding - an Ellis /31
17 hord Recognition chroma bin E Beat synchronous chroma look like chords E- B-- --E ---F... can we transcribe them? time / sec Two approaches manual templates (prior knowledge) learned models (from training data) ata-riven Music Understanding - an Ellis /31
18 hord Recognition System nalogous to speech recognition aussian models of features for each chord Hidden Markov Models for chord transitions Sheh & Ellis 2003 Ellis & Weller 2010 Beat track udio Hz BPF hroma beat-synchronous chroma features HMM Viterbi chord labels test train Hz BPF hroma Root normalize aussian Unnormalize 24 auss models B E B E maj E B c min Labels Resample b E B a g ount transitions 24x24 transition matrix f e d c B F E E F B c d e f g a b ata-riven Music Understanding - an Ellis /31
19 hord Recognition Often works: udio freq / Hz Let It Be/06-Let It Be 240 round truth chord Beatsynchronous chroma Recognized E F B :min :min/b7 F:maj7 F:maj6 F :min a F F a :min/b7 F:maj But only 60-80% of the time ata-riven Music Understanding - an Ellis /31
20 What did the models learn? hord model centers (means) indicate chord templates : PP_ROT family model means (train18) IM OM7 MJ MIN MIN E F B (for -root chords) ata-riven Music Understanding - an Ellis /31
21 freq / khz Finding over Songs Little similarity in surface audio... Let It Be - The Beatles Let It Be / Beatles / verse 1 freq / khz Ellis & Poliner 07 Ravuri & Ellis 10 Let It Be - Nick ave Let It Be / Nick ave / verse time / sec time / se.. but appears in beat-chroma Beat-sync chroma features Beat-sync chroma features chroma F chroma F beats beat ata-riven Music Understanding - an Ellis /31
22 Large-Scale over Recognition 2 Fourier Transform Magnitude (2FTM) fixed-size feature to capture essence of chromagram: Bertin-Mahieux & Ellis 12 First results on finding covers in 1M songs verage rank meanp random 500, jumpcodes 2 308, FTM (50 P) 137, ata-riven Music Understanding - an Ellis /31
23 Finding ommon Fragments luster beat-synchronous chroma patches chroma bins F F F F # instances # instances # instances # instances # instances # instances # instances a20-top10x5-cp4-4p0 # instances time / beats ata-riven Music Understanding - an Ellis /31
24 lustered Fragments chroma bins F depeche mode 13-Ice Machine s roxette 03-Fireworks s F roxette 04-Waiting For The Rain s tori amos 11-Playboy Mommy s time / beats... for a dictionary of common themes? ata-riven Music Understanding - an Ellis /31
25 4. Example pplications: Music iscovery Berenzweig & Ellis 03 onnecting listeners to musicians ata-riven Music Understanding - an Ellis /31
26 Playlist eneration Mandel, Poliner, Ellis 06 Incremental learning of listeners preferences ata-riven Music Understanding - an Ellis /31
27 MajorMiner: Music Tagging escribe music using words Mandel & Ellis 07, 08 ata-riven Music Understanding - an Ellis /31
28 lassification Results lassifiers trained from top 50 tags 01 Soul Eyes freq / Hz _90s club trance end drum_bass singing horns punk samples silence quiet noise solo strings indie house alternative r_b funk soft ambient british distortion drum_machine country keyboard saxophone fast instrumental electronica 80s voice beat slow rap hip_hop jazz piano techno dance female bass vocal pop electronic rock synth male guitar drum time / s ata-riven Music Understanding - an Ellis /
29 Music Transcription feature representation Poliner & Ellis 05, 06, 07 feature vector Training data and features: MII, multi-track recordings, playback piano, & resampled audio (less than 28 mins of train audio). Normalized magnitude STFT. classification posteriors lassification: N-binary SVMs (one for ea. note). Independent frame-level classification on 10 ms grid. ist. to class bndy as posterior. hmm smoothing Temporal Smoothing: Two state (on/off) independent HMM for ea. note. Parameters learned from training data. Find Viterbi sequence for ea. note. ata-riven Music Understanding - an Ellis /31
30 MEPsoft Music Engineering rt Projects collaboration between EE and omputer Music enter with ouglas Repetto, Ron Weiss, and the rest of the MEP team ata-riven Music Understanding - an Ellis /31
31 onclusions Low-level features lassification and Similarity browsing discovery production Music audio Melody and notes Key and chords Tempo and beat Music Structure iscovery modeling generation curiosity Lots of data + noisy transcription + weak clustering musical insights? ata-riven Music Understanding - an Ellis /31
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