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Metric Learning for Music Discovery with

Source and Target Playlists
Ying-Shu Kuo

August 12 2015
Proposed Idea
No Name Artist
1 Song_A Artist_A
2 Song_B Artist_B
3 Song_C Artist_A
4 Song_D Artist_C
5 Song_E Artist_B
6 S...
Use Case
Parameter • Explore unknown music genre

(e.g. from Jazz to Metal)
• Get to know your friend’s jam

(e.g. from yo...
No Name Artist
1 Song_A Artist_A
2 Song_B Artist_B
3 Song_C Artist_A
4 Song_D Artist_C
5 Song_E Artist_B
6 Song_F Artist_D...
Million Song Dataset
• Criteria for a good dataset
• Why use MSD?
Bertin-Mahieux, Thierry, et al. "The million song datase...
EchoNest Feature
• Metadata: artist name / song title / album name /

year / duration
• Low-level: segment time / loudness...
EchoNest Feature
•
•
• Codebook-based
mean
mean
stddev
mean
stddev
Metric Learning
• Metric: define the way you measure the distance
between data
http://en.wikipedia.org/wiki/File:Manhattan_...
Metric Learning
• Mahanalobis Distance
http://stats.stackexchange.com/questions/62092/bottom-to-top-explanation-of-the-mah...
Metric Learning
• Metric Learning: learning distance function
Bellet, Aurélien, Amaury Habrard, and Marc Sebban. "A survey...
Metric Learning
• Why I need to reshape the feature space?
original metric learned
Metric Learning – LMNN
Large Margin Nearest Neighbor
Weinberger, Kilian Q., John Blitzer, and Lawrence K. Saul. "Distance ...
Metric Learning – GB-LMNN
Gradient-Boosted Large Margin Nearest Neighbor
Kedem, Dor, Zhixiang Eddie Xu, and Kilian Q. Wein...
Metric Learning – Evaluation
• Does starting / ending songs cluster?
• Davies–Bouldin Index
Metric Learning – Evaluation
10 vs 10 ø LMNN GB-LMNN OASIS
average 9.46 10.85 5.62 12.49
max – 16.43 15.66 13.25
min – 8.8...
Dimension Reduction
• High dimension to low dimension based on constraints
• Keep the distance between data the same
• 2-D...
Dimension Reduction – t-SNE
http://commons.wikimedia.org/wiki/File:T_distribution_1df_enhanced.svg
Van der Maaten, Laurens...
Playlist Generation
• Trying to create a list of music based on some
assumptions/rules/constraints.
Playlist Generation – Related Work
Zheleva et al.
[1]
McFee et al.
[2]
Chen et al.

[3]
mine
assumption /
constraint
match...
Playlist Generation – Related Work
Flexer [4] Van Gulik [5] Lamere [6] mine
assumption /
constraint
specifying
start and e...
Playlist Generation – Method
• number of songs
• threshold
http://www.pstcc.edu/departments/natural_behavioral_sciences/We...
Playlist Generation – Result
• demo
Future Work and Discussion
• Discussion
• feature representation
• path finding
• Future Work
• Implementation on Spotify A...
Thank you!
Questions / Comments?
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Metric Learning for Music Discovery with Source and Target Playlists

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Published on

Playlist generation for music exploration by defining sets of source songs and target songs and deriving a playlist through metric learning and boundary constraints.
https://github.com/hank5925/mlmdstp

Published in: Software
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Metric Learning for Music Discovery with Source and Target Playlists

  1. 1. Metric Learning for Music Discovery with
 Source and Target Playlists Ying-Shu Kuo August 12 2015
  2. 2. Proposed Idea No Name Artist 1 Song_A Artist_A 2 Song_B Artist_B 3 Song_C Artist_A 4 Song_D Artist_C 5 Song_E Artist_B 6 Song_F Artist_D 7 Song_G Artist_E 8 Song_H Artist_E 9 Song_I Artist_F Playlist Your Set Target Set Search Song_A Artist_A Parameter = Song = Your Set = Target Set = Others = Chosen
 Playlist = Similarity ※ x-y axis has no meaning
  3. 3. Use Case Parameter • Explore unknown music genre
 (e.g. from Jazz to Metal) • Get to know your friend’s jam
 (e.g. from your favs to her favs)
  4. 4. No Name Artist 1 Song_A Artist_A 2 Song_B Artist_B 3 Song_C Artist_A 4 Song_D Artist_C 5 Song_E Artist_B 6 Song_F Artist_D 7 Song_G Artist_E 8 Song_H Artist_E 9 Song_I Artist_F Playlist Your Set Target Set Search Song_A Artist_A Parameter What I need for this 1. Song to play with => Million Song Dataset / Spotify API 2. Music similarity => EchoNest Audio Features 3. Cluster song sets => Metric Learning 4. 2-D Visualization => Dimension Reduction 5. Playlist Generation
  5. 5. Million Song Dataset • Criteria for a good dataset • Why use MSD? Bertin-Mahieux, Thierry, et al. "The million song dataset." ISMIR 2011: Proceedings of the 12th International Society for Music Information Retrieval Conference, October 24-28, 2011, Miami, Florida. University of Miami, 2011. http://audiocontentanalysis.org/data-sets Dataset RWC CAL500 GTZAN MusiCLEF MSD size 465 502 1,000 200,000 1,000,000 has audio Y Y Y Y N* has metadata Y Y Y (update) ? Y * A partial of it has 7digital audio preview. All of the songs have content-based features.
  6. 6. EchoNest Feature • Metadata: artist name / song title / album name /
 year / duration • Low-level: segment time / loudness / pitch / timbre • Time: tempo / time signature / section time / bar time … http://developer.echonest.com/docs/v4/_static/AnalyzeDocumentation.pdf
  7. 7. EchoNest Feature • • • Codebook-based mean mean stddev mean stddev
  8. 8. Metric Learning • Metric: define the way you measure the distance between data http://en.wikipedia.org/wiki/File:Manhattan_distance.svg Bellet, Aurélien, Amaury Habrard, and Marc Sebban. "A survey on metric learning for feature vectors and structured data." arXiv preprint arXiv:1306.6709 (2013).
  9. 9. Metric Learning • Mahanalobis Distance http://stats.stackexchange.com/questions/62092/bottom-to-top-explanation-of-the-mahalanobis-distance
  10. 10. Metric Learning • Metric Learning: learning distance function Bellet, Aurélien, Amaury Habrard, and Marc Sebban. "A survey on metric learning for feature vectors and structured data." arXiv preprint arXiv:1306.6709 (2013).
  11. 11. Metric Learning • Why I need to reshape the feature space? original metric learned
  12. 12. Metric Learning – LMNN Large Margin Nearest Neighbor Weinberger, Kilian Q., John Blitzer, and Lawrence K. Saul. "Distance metric learning for large margin nearest neighbor classification." Advances in neural information processing systems. 2005. NOT the unlabeled one!!!
  13. 13. Metric Learning – GB-LMNN Gradient-Boosted Large Margin Nearest Neighbor Kedem, Dor, Zhixiang Eddie Xu, and Kilian Q. Weinberger. "Gradient Boosted Large Margin Nearest Neighbors." • Kernel trick, non-linear • Gradient Boosted Regression Tree
  14. 14. Metric Learning – Evaluation • Does starting / ending songs cluster? • Davies–Bouldin Index
  15. 15. Metric Learning – Evaluation 10 vs 10 ø LMNN GB-LMNN OASIS average 9.46 10.85 5.62 12.49 max – 16.43 15.66 13.25 min – 8.89 0.61 11.99
  16. 16. Dimension Reduction • High dimension to low dimension based on constraints • Keep the distance between data the same • 2-D visualization Van der Maaten, Laurens, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of Machine Learning Research 9.2579-2605 (2008): 85.
  17. 17. Dimension Reduction – t-SNE http://commons.wikimedia.org/wiki/File:T_distribution_1df_enhanced.svg Van der Maaten, Laurens, and Geoffrey Hinton. "Visualizing data using t-SNE." Journal of Machine Learning Research 9.2579-2605 (2008): 85. • Pairwise distance • Effective neighbors = local • Gaussian vs t-distribution
  18. 18. Playlist Generation • Trying to create a list of music based on some assumptions/rules/constraints.
  19. 19. Playlist Generation – Related Work Zheleva et al. [1] McFee et al. [2] Chen et al. [3] mine assumption / constraint matching user taste and song taste natural language natural language 2 clusters, smooth input (dataset) triplet
 (user, song, t) tag 0/1; content- based playlists content- based approach topic model
 (LDA) Markov chain ensemble Markov chain nearest neighbors evaluation entropy- based log likelihood log likelihood ? [1] Zheleva, Elena, et al. "Statistical models of music-listening sessions in social media." Proceedings of the 19th international conference on World wide web. ACM, 2010. [2] McFee, Brian, and Gert RG Lanckriet. "The Natural Language of Playlists." ISMIR. 2011. [3] Chen, Shuo, et al. "Playlist prediction via metric embedding." Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012.
  20. 20. Playlist Generation – Related Work Flexer [4] Van Gulik [5] Lamere [6] mine assumption / constraint specifying start and end high-level control of playlist boil the frog 2 clusters, smooth input (dataset) content- based songs with metadata songs with artist info content- based approach divergence ratio visualization path drawing artist similarity nearest neighbors evaluation same genre – – ? [4] Flexer, Arthur, et al. "Playlist Generation using Start and End Songs." ISMIR. 2008. [5] Van Gulik, Rob, and Fabio Vignoli. "Visual Playlist Generation on the Artist Map." ISMIR. Vol. 5. 2005. [6] http://static.echonest.com/frog/
  21. 21. Playlist Generation – Method • number of songs • threshold http://www.pstcc.edu/departments/natural_behavioral_sciences/Web%20Physics/E2020D0103.gif
  22. 22. Playlist Generation – Result • demo
  23. 23. Future Work and Discussion • Discussion • feature representation • path finding • Future Work • Implementation on Spotify API • User Study
  24. 24. Thank you! Questions / Comments?

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