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Palco3.0

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Palco3.0

  1. 1. Outline Music Recommendation Approaches Examples Conclusions and future work Recent Development at INESC Porto on Palco Principal 3.0 Beatriz Mora May 13, 2010
  2. 2. Outline Music Recommendation Approaches Examples Conclusions and future work Music Recommendation Approaches Content-based Context-based Hybrid Examples Conclusions and future work
  3. 3. Outline Music Recommendation Approaches Examples Conclusions and future work Content-based: Music signal • Frequency domain. • Timbral descriptors extracted from the music audio signal.
  4. 4. Outline Music Recommendation Approaches Examples Conclusions and future work Content-based: Distance audio matrix • Euclidean distance. • Matrix of distances between all songs. • Musical matching. • Sorted distance song rank.
  5. 5. Outline Music Recommendation Approaches Examples Conclusions and future work Context-based: Latent Semantic Analysis (LSA) is a technique used in natural language processing, in particular in vectorial semantics, for analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms, Wikipedia. • Word correlations. • Reduce dimension.
  6. 6. Outline Music Recommendation Approaches Examples Conclusions and future work Context-based: Latent Semantic Analysis (LSA) • Word: Tag. • Concept: Group of tags.
  7. 7. Outline Music Recommendation Approaches Examples Conclusions and future work Hybrid: weighted and cascade Hybrid is a combination of the two previous approaches. There are two methods: • Hybrid-weighted.
  8. 8. Outline Music Recommendation Approaches Examples Conclusions and future work Hybrid: weighted and cascade Hybrid is a combination of the two previous approaches. There are two methods: • Hybrid-weighted. • Hybrid-cascade.
  9. 9. Outline Music Recommendation Approaches Examples Conclusions and future work Jazz seed song Content-based LSA-based Hybrid-weighted Hybrid-cascade
  10. 10. Outline Music Recommendation Approaches Examples Conclusions and future work Jazz seed song Content-based LSA-based Same timbre content Same band Different genre Same genre Hybrid-weighted Hybrid-cascade New bands Same band as seed song Same genre Same genre
  11. 11. Outline Music Recommendation Approaches Examples Conclusions and future work Rock seed song Content-based LSA-based Hybrid-weighted Hybrid-cascade
  12. 12. Outline Music Recommendation Approaches Examples Conclusions and future work Rock seed song Content-based LSA-based Same genre Same band Hybrid-weighted Hybrid-cascade New bands New bands Same genre
  13. 13. Outline Music Recommendation Approaches Examples Conclusions and future work Conclusions • Audio is relevant and gives us a very wide music recommendation.
  14. 14. Outline Music Recommendation Approaches Examples Conclusions and future work Conclusions • Audio is relevant and gives us a very wide music recommendation. • LSA limits the recommendations by “our” concepts.
  15. 15. Outline Music Recommendation Approaches Examples Conclusions and future work Conclusions • Audio is relevant and gives us a very wide music recommendation. • LSA limits the recommendations by “our” concepts. • Which kind of music would we like to discover?.
  16. 16. Outline Music Recommendation Approaches Examples Conclusions and future work Future work • Optimize audio analysis.
  17. 17. Outline Music Recommendation Approaches Examples Conclusions and future work Future work • Optimize audio analysis. • Look for new descriptors in the audio that can help to distinguish genres.
  18. 18. Outline Music Recommendation Approaches Examples Conclusions and future work Future work • Optimize audio analysis. • Look for new descriptors in the audio that can help to distinguish genres. • Collaborative filtering.

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