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Hypergraph Models of Playlist Dialects

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Hypergraph Models of Playlist Dialects

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Playlist generation is an important task in music information retrieval. While previous work has treated a playlist
collection as an undifferentiated whole, we propose to build
playlist models which are tuned to specific categories or
dialects of playlists. Toward this end, we develop a general
class of flexible and scalable playlist models based upon
hypergraph random walks. To evaluate the proposed models, we present a large corpus of categorically annotated,
user-generated playlists. Experimental results indicate that
category-specific models can provide substantial improvements in accuracy over global playlist models.

Playlist generation is an important task in music information retrieval. While previous work has treated a playlist
collection as an undifferentiated whole, we propose to build
playlist models which are tuned to specific categories or
dialects of playlists. Toward this end, we develop a general
class of flexible and scalable playlist models based upon
hypergraph random walks. To evaluate the proposed models, we present a large corpus of categorically annotated,
user-generated playlists. Experimental results indicate that
category-specific models can provide substantial improvements in accuracy over global playlist models.

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Hypergraph Models of Playlist Dialects

  1. 1. Hypergraph models of playlist dialects Brian McFee Lab Center for Jazz Studies/LabROSA Columbia University ROSA Laboratory for the Recognition and Organization of Speech and Audio Gert Lanckriet Electrical & Computer Engineering University of California, San Diego
  2. 2. Automatic playlist generation
  3. 3. Evaluating playlist algorithms [M. & Lanckriet, 2011] ... 2. Compute playlist 1. Observe playlists from users likelihoods ? > 3. Compare algorithms by likelihood scores
  4. 4. Evaluating playlist algorithms [M. & Lanckriet, 2011] Key idea: Playlist algorithm = Probability distribution over song sequences
  5. 5. Modeling playlist diversity Playlists
  6. 6. Modeling playlist diversity Road trip Mixed Genre Party mix Hip-hop
  7. 7. Data collection http://www.artofthemix.org/ Started in 1998, users upload and share playlists [Ellis, Whitman, Berenzweig, and Lawrence, ISMIR 2002]
  8. 8. The data: AotM-2011 • 98K songs indexed to Million Song Dataset • 87K playlists (1998-2011), ~210K contiguous segments • 40 playlist categories, user meta-data available
  9. 9. # Playlists per category Mixed genre Theme Rock-pop Alternating DJ Indie Single artist Romantic Road trip Depression Punk Break-up Narrative Hip-hop Sleep Dance-house Electronic Rhythm & blues Country Cover Hardcore Rock Jazz Folk Ambient Blues 100 1000 104 105
  10. 10. # Playlists per category Mixed genre Theme Rock-pop Alternating DJ Indie Single artist Romantic Road trip Depression Punk Break-up Narrative Hip-hop Sleep Dance-house Electronic • Majority of playlists are Mixed genre Rhythm & blues Country Cover Hardcore • Remaining categories: Rock Jazz Folk contextual/mood, genre, other Ambient Blues 100 1000 104 105
  11. 11. Our goals • Which categories can we model? Are some harder than others? • Which features are useful for playlist generation? • Do transitions matter? Are some categories less diverse?
  12. 12. A simple playlist model 1. Start with a set of songs
  13. 13. A simple playlist model 2. Select a subset (e.g., jazz songs)
  14. 14. A simple playlist model 3. Select a song
  15. 15. A simple playlist model 4. Find subsets containing the current song
  16. 16. A simple playlist model 4. Select a new subset
  17. 17. A simple playlist model 5. Select a new song
  18. 18. A simple playlist model 6. Repeat...
  19. 19. A simple playlist model 6. Repeat...
  20. 20. Connecting the dots... • Random walk on a hypergraph - Vertices = songs - Edges = subsets
  21. 21. Connecting the dots... • Random walk on a hypergraph - Vertices = songs - Edges = subsets • Learning: optimize edge weights from example playlists
  22. 22. Connecting the dots... • Random walk on a hypergraph - Vertices = songs - Edges = subsets • Learning: optimize edge weights from example playlists • Sampling is efficient, edge labels provide transparency
  23. 23. The hypergraph random walk model exp. prior edge weights transitions playlists
  24. 24. Edge construction: example • Audio: cluster songs by timbre
  25. 25. Edge construction: example • Audio: cluster songs by timbre Audio-1 Audio-2 Audio-4 Audio-3 • Multiple clusterings (k=16, 64, 256)
  26. 26. Edge construction: the kitchen sink • Audio • MSD taste profile • Era • Familiarity • Lyrics • Social tags • Uniform shuffle • Conjunctions: "TAG_jazz-&-YEAR_1959" • 6390 edges, 98K vertices (songs)
  27. 27. Evaluation protocol • Repeat x10: - Split playlist collection into 75% train/25% test - Learn edge weights on training playlists - Evaluate average likelihood of test playlists • Compare gain in likelihood over uniform shuffle baseline
  28. 28. Experiment 1: global vs. categorical • Fit one model per category • Fit one global model to all categories • Test on each category and compare likelihoods • Question: When does categorical training improve accuracy?
  29. 29. Experiment 1: global vs. categorical Unifo rm ALL Mixed Global model Theme Category-specific Rock-pop Alternating DJ Indie Single artist Romantic Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues 0% 5% 10% 1 5% 20% 25% Log-likelihood gain over uniform shuffle
  30. 30. Experiment 1: global vs. categorical Unifo • Largest gains for genre playlists rm ALL Mixed • No change for "hard" categories Global model Theme Category-specific Rock-pop Alternating DJ (e.g., Mixed, Alternating DJ, Theme) Indie Single artist Romantic Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues 0% 5% 10% 1 5% 20% 25% Log-likelihood gain over uniform shuffle
  31. 31. Experiment 1: learned edge weights ALL Mixed Theme Rock-pop Alternating DJ Indie Single Artist Romantic RoadTrip Punk Depression Break Up Narrative Hip-hop Sleep Electronic music Dance-house Rhythm and Blues Country Cover Hardcore Rock Jazz Folk Reggae Blues Audio CF Era Familiarity Lyrics Tags Uniform
  32. 32. Experiment 2: continuity? • Do we need to model playlist continuity? edge weights songs • Simplified model: - ignore transitions - choose each edge IID exp. prior playlists • Question: Are some categories more diverse than others?
  33. 33. Experiment 2: continuity Unifo rm ALL Mixed Global model Theme Category-specific Rock-pop Alternating DJ Indie Single artist Romantic Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues -15% -10% -5% 0% 5% 10% 15% 20% Log-likelihood gain over uniform shuffle
  34. 34. Experiment 2: continuity Unifo rm ALL Mixed Global model Theme Category-specific • Most categories exhibit both Rock-pop Alternating DJ Indie continuity AND diversity Single artist Romantic • Transitions are important! Road trip Punk Depression Break up Narrative Hip-hop Sleep Electronic Dance-house R&B Country Cover songs Hardcore Rock Jazz Folk Reggae Blues -15% -10% -5% 0% 5% 10% 15% 20% Log-likelihood gain over uniform shuffle
  35. 35. Example playlists Rhythm & Blues EDGE SONG 70s & soul Lyn Collins - Think Audio #14 & funk Isaac Hayes - No Name Bar DECADE 1965 & soul Michael Jackson - My Girl Electronic music EDGE SONG Audio #11 & downtempo Everything but the Girl - Blame DECADE 1990 & trip-hop Massive Attack - Spying Glass Audio #11 & electronica Björk - Hunter
  36. 36. Conclusions • Category-specific models outperform global playlist models. • Continuity matters! • Proposed model is simple, efficient, and transparent • AotM-2011 dataset available now! http://cosmal.ucsd.edu/cal/projects/aotm2011
  37. 37. Obrigado!

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