Metric Learning for Music Discovery with Source and Target Playlists
•
2 gostaram•1,190 visualizações
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
Recomendados
Recomendados
Mais conteúdo relacionado
Semelhante a Metric Learning for Music Discovery with Source and Target Playlists
Semelhante a Metric Learning for Music Discovery with Source and Target Playlists (20)
Último
Último (20)
Metric Learning for Music Discovery with Source and Target Playlists
- 1. Metric Learning for Music Discovery with Source and Target Playlists Ying-Shu Kuo August 12 2015
- 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. 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. 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. 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. 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
- 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. Metric Learning • Mahanalobis Distance http://stats.stackexchange.com/questions/62092/bottom-to-top-explanation-of-the-mahalanobis-distance
- 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. Metric Learning • Why I need to reshape the feature space? original metric learned
- 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. 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. Metric Learning – Evaluation • Does starting / ending songs cluster? • Davies–Bouldin Index
- 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. 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. 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. Playlist Generation • Trying to create a list of music based on some assumptions/rules/constraints.
- 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. 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. Playlist Generation – Method • number of songs • threshold http://www.pstcc.edu/departments/natural_behavioral_sciences/Web%20Physics/E2020D0103.gif
- 22. Playlist Generation – Result • demo
- 23. Future Work and Discussion • Discussion • feature representation • path finding • Future Work • Implementation on Spotify API • User Study
- 24. Thank you! Questions / Comments?