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More Like This:Machine Learning Approaches
to Music Similarity
Brian McFee
Computer Science & EngineeringUniversity of California, San Diego
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Music discovery in days of yore...
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Music discovery 2.0: the present
f
• ~20 million songs available
• Discovery is still largely human-powered
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A Google for music?
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A Google for music?
• Standard text search can work with meta-data• Can we predict meta-data from audio? ⁃ [Turnbull, 2008], [Barrington, 2011]
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Query by example
• Natural, user-friendly alternative to text search
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Query by example
• Natural, user-friendly alternative to text search
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Query by example
• Natural, user-friendly alternative to text search
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This talk
• Learning algorithms for QBE, geared toward music discovery
• We'll look at two consumption models:
• Evaluation derived from user behavior
Passive listening(playlist generation)
Active browsing(search & ranking)
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Learning similarity
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Defining similarity: semantics?
Song similarity=
tag similarity?
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Defining similarity: semantics?
• Drawbacks: - Choosing, weighting vocabulary is surprisingly difficult - Hard to maintain quality at scale
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Defining similarity: human judgements?
• Which is more similar?[M. & Lanckriet, 2009, 2011]
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Defining similarity: human judgements?
• Which is more similar?
• Drawbacks: ambiguity, subjectivity, scale
[M. & Lanckriet, 2009, 2011]
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Collaborative filter similarity
• Collect listening histories for (lots of!) users
• Song similarity = portion of users in common
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Collaborative filter similarity
• Collaborative filters perform well... - ... for tagging [Kim, Tomasik, & Turnbull, 2009] - ... and playlisting [Barrington, Oda, & Lanckriet, 2009] - ... and recommendation (Yahoo, Last.fm, iTunes...)
• Implicit feedback requires no additional effort from users
• ... but fails on unpopular items: the cold start problem!
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Learning from a collaborative filter[M., Barrington, & Lanckriet, 2010, 2012]
1.
2.
3.
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Learning from a collaborative filter[M., Barrington, & Lanckriet, 2010, 2012]
1.
2.
3.
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Learning from a collaborative filter[M., Barrington, & Lanckriet, 2010, 2012]
1.
2.
3.
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Rankings in audio space
Rankings in CF space
=
Metric learning to rank
• The goal:
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Ranking by (learned) distance
Targetrankings
=
Metric learning to rank
• The goal:[M. & Lanckriet, 2010]
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Ranking by (learned) distance
Targetrankings
=
Metric learning to rank
• The goal:
• Optimize a linear transformation for ranking
[M. & Lanckriet, 2010]
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Structure prediction: nearest neighbors
• Setup: database , rankings
• PSD matrix transforms features
• Order by distance from :
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Structure prediction: nearest neighbors
• Setup: database , rankings
• PSD matrix transforms features
• Order by distance from :
• encodes each (query, ranking) pair
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Metric learning to rank (MLR)
Score fortarget ranking
Score for anyother ranking
Predictionerror
+>
• Supported losses Δ: AUC, KNN, MAP, MRR, NDCG, Prec@k
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MLR solver• Cutting-plane algorithm based on 1-slack Structural SVM [Joachims, et al. 2009]
• Repeat until convergence:
Constraintgeneration
(DP)
Semi-definiteprogramming
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MLR solver• Cutting-plane algorithm based on 1-slack Structural SVM [Joachims, et al. 2009]
• Repeat until convergence:
Constraintgeneration
(DP)
Semi-definiteprogramming
Sequence of QPs
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MLR solver• Cutting-plane algorithm based on 1-slack Structural SVM [Joachims, et al. 2009]
• Repeat until convergence:
• Multiple kernel extensions: [Galleguillos, M., Belongie, & Lanckriet 2011]
Constraintgeneration
(DP)
Semi-definiteprogramming
Sequence of QPs
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Audio pipeline
Audio signal
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Audio pipeline
Audio signal 1. Feature extraction
Bag of ΔMFCCs
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Audio pipeline
Audio signal 1. Feature extraction
Bag of ΔMFCCs
Codeword hist.
2. Vector quantization
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Audio pipeline
Audio signal
PPK
1. Feature extraction
Bag of ΔMFCCs
Codeword hist.
2. Vector quantization
3. Probability product kernel
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Audio pipeline
Audio signal
PPK
CF similarity
MLR
Supervision
Features
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Evaluation: CAL10K
• Last.fm collaborative filter - 360K users, 186K artists
• CAL10K songs - 5.4K songs, 2K artists (after CF matching)
[Celma, 2008]
[Tingle, Turnbull, & Kim, 2010]
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Evaluation: CAL10K
• Last.fm collaborative filter - 360K users, 186K artists
• CAL10K songs - 5.4K songs, 2K artists (after CF matching)
• Evaluation: - Split artists into train/val/test - Target rankings: top-10 most similar train artists
[Celma, 2008]
[Tingle, Turnbull, & Kim, 2010]
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Evaluation: comparison
• Gaussian mixture models + KL divergence - 8 component, diagonal covariance GMM per song
• Auto-tags: predict 149 semantic tags from audio [Turnbull, 2008]
• [Our method] VQ+MLR: 1024 codewords
• Expert tags: 1053 tags from Pandora [Tingle, et al., 2009]
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Similarity learning: results
GMM (KL)
Auto-tags
Auto-tags + MLR
Audio VQ
Audio VQ + MLR
Expert tags (cos)
Expert tags + MLR0.65 0.70 0.75 0.80 0.85 0.90 0.95
AUC
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Example playlists
The Ramones - Go Mental
Def Leppard - Promises The Buzzcocks - Harmony In My Head Los Lonely Boys - Roses Wolfmother - Colossal Judas Priest - Diamonds and Rust (live)
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Example playlists
The Ramones - Go Mental
Def Leppard - Promises The Buzzcocks - Harmony In My Head Los Lonely Boys - Roses Wolfmother - Colossal Judas Priest - Diamonds and Rust (live)
The Buzzcocks - Harmony In My Head Mötley Crüe - Same Ol' Situation The Offspring - Gotta Get Away The Misfits - Skulls AC/DC - Who Made Who (live)
MLR
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Example playlists
Fats Waller - Winter Weather
Dizzy Gillespie - She's Funny That WayEnrique Morente - SoleaChet Atkins - In the MoodRachmaninov - Piano Concerto #4Eluvium - Radio Ballet
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Example playlists
Fats Waller - Winter Weather
Dizzy Gillespie - She's Funny That WayEnrique Morente - SoleaChet Atkins - In the MoodRachmaninov - Piano Concerto #4Eluvium - Radio Ballet
Chet Atkins - In the MoodCharlie Parker - What Is This Thing Called Love?Bud Powell - OblivionBob Wills & His Texas Playboys - Lyla LouBob Wills & His Texas Playboys - Sittin' On Top of the World
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Scaling up: fast retrieval
• Audio similarity search for a million songs?
• Idea: Index data with spatial trees
• 100-NN search over 900K songs: - Brute force: 2.4s - 50% recall: 0.14s 17x speedup - 20% recall: 0.02s 120x speedup
[M. & Lanckriet, 2011]
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Similarity learning: summary
• Collaborative filters provide user-centric music similarity
• CF similarity can be approximated by audio features
• Audio search can be done quickly at large-scale
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Playlist generation
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Playlist generation
• Goal: generate a "good" song sequence - Music auto-pilot (given context)
• Many existing algorithms, but no standard evaluation
• What makes one algorithm better than another?
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Playlist evaluation 1: Human survey
• Idea: generate playlists, ask for opinions
• Impractical at large-scale: - Huge search space - User taste, expertise can be problematic - Slow, expensive
• Does not facilitate rapid evaluation and optimization
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Playlist evaluation 2: Information retrieval
• Idea: - Define "good" and "bad" playlists - Predict the next song, measure accuracy
• But what makes a bad playlist?
• Do users agree on good/bad?
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A generative approach
• Playlist algorithm = distribution over playlists
• Don't evaluate synthetic playlists
• Do evaluate the likelihood of generating real playlists
[M. & Lanckriet, 2011b]
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The playlist collection: AOTM-2011
• Art of the Mix - 13 years of playlists - ~210K playlist segments - ~100K songs from MSD
• Top 25 playlist categories: - Genre: Punk, Hip-hop, Reggae... - Context: Road trip, Break-up, Sleep... - Other: Mixed genre, Alternating DJ...
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A simple playlist model
1. Start with a set of songs
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A simple playlist model
2. Select a subset (e.g., jazz songs)
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A simple playlist model
3. Select a song
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A simple playlist model
4. Select a new subset
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A simple playlist model
4. Select a new subset
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A simple playlist model
5. Select a new song
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A simple playlist model
6. Repeat...
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A simple playlist model
6. Repeat...
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Connecting the dots...
• Random walk on a hypergraph - Vertices = songs - Edges = subsets
• Edges derived from: - Audio clusters, tags, lyrics, era, popularity, CF - or combinations/intersections
• Goal: optimize edge weights from example playlists
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Playlist model
exp. prior
playlists
transitions
edge weights
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Playlist generation: evaluation
• Setup: - Split playlist collection into train/test - Learn edge weights on training playlists - Evaluate average likelihood of test playlists
• Train per category, or all together
• Compare against uniform shuffle baseline
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Random walk results
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single artistRomanticRoad trip
PunkDepression
Break upNarrativeHip-hop
SleepElectronic
Dance-houseR&B
CountryCover songs
HardcoreRockJazzFolk
ReggaeBlues
0% 5% 10% 1 5% 20% 25%
Log-likelihood gain over random shuffle
Global modelCategory-specific
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Stationary model results
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single artistRomanticRoad trip
PunkDepression
Break upNarrativeHip-hop
SleepElectronic
Dance-houseR&B
CountryCover songs
HardcoreRockJazzFolk
ReggaeBlues
Log-likelihood gain over random shuffle
-15% -10% -5% 0% 5% 10% 15% 20%
Global modelCategory-specific
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Example playlists
70s & soulAudio #14 & funkDECADE 1965 & soul
Lyn Collins - ThinkIsaac Hayes - No Name BarMichael Jackson - My Girl
Audio #11 & downtempoDECADE 1990 & trip-hopAudio #11 & electronica
Everything But The Girl - BlameMassive Attack - Spying GlassBjörk - Hunter
Rhythm & Blues
Electronic music
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Playlist generation summary
• Generative approach simplifies evaluation
• AOTM-2011 collection facilitates learning and evaluation
• Robust, efficient and transparent feature integration
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The future
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Directions for future work
• Audio features: coding, dynamics and rhythm
• Playlist models: mixtures, long-range interactions
• UI models: interactive, context-aware, diversity
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Personalized recommendation
• The Million Song Dataset Challenge
• Listening histories for 1.1M users, 380K songs
• Task: personalized song recommendation
[M., Bertin-Mahieux, Ellis, & Lanckriet, 2012]
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Conclusion
• MLR can optimize distance metrics for ranking, QBE retrieval
• Audio similarity can approximate a collaborative filter
• Generative playlist model integrates data, models dynamics
• User-centric evaluation makes it all possible
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Thanks!
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Metric partial order feature
• Score is large when distances match ranking
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Playlist weights: 6390 edges
Audio CF Era Familiarity Lyrics Tags Uniform
ALLMixed
ThemeRock-pop
Alternating DJIndie
Single ArtistRomanticRoadTrip
PunkDepression
Break UpNarrativeHip-hop
SleepElectronic music
Dance-houseRhythm and Blues
CountryCover
HardcoreRockJazzFolk
ReggaeBlues
• Audio & CF: k-means (16/64/256)• Era: year, decade, decade+5• Familiarity: high/med/low
• Lyrics: LDA (k=32, top-1/3/5)• Tags: Last.fm top-10• Conjunctions