Annotating Music Collections: How Content-Based Similarity Helps to Propagate Tags

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    Annotating Music Collections: How Content-Based Similarity Helps to Propagate Tags - Presentation Transcript

    1. Annotating Music Collections: How Content-Based Similarity Helps to Propagate Labels
        • Mohamed Sordo, Cyril Laurier and Òscar Celma
        • (Music Technology Group, UPF)
    2. Outline
      • Introduction
      • Motivation and goals
      • Evaluation
      • Demonstration
      • Conclusions
    3. introduction
      • Massive Increase in volume of online music
      • Long Tail Economics
        • Make everything available
        • Help me find it
    4. introduction:: last year's work...
      • Searchsounds (ISMIR, 2006)
        • Exploits MP3-blogs info
        • Natural Lang. Processing
        • Audio similarity (“more like this”)
    5. motivation:: annotating music collections
      • Tag sparsity
        • Postal Service - “Such Great Heights”
          • 2.4 million num. plays
          • 100s of tags
        • Mike Shupps's - “All Over Town”
          • 3 num. plays
          • 0 tags
      • Cold start problem for new songs
    6. goals
      • Ease the process of annotating large music collections
        • No learning from audio to tags, but...
        • Content-based similarity to propose tags among songs
          • Timbre, rhythm, tonality, etc.
    7. our proposal Annotated
    8. our proposal
      • “wisdom of (song) crowds”
      FOR FREE!!! Annotated
    9. our proposal
      • evaluation...? “wisdom of crowds” & feedback
      FOR FREE!!! Annotated
    10. our proposal
        • Relevance feedback from the users
          • Spencer Tunick's “wisdom of crowds”
      http://www.flickr.com/photos/guacamoleproject/487376758/
    11. our proposal
      • new song, no tags
      rock Indie Cute guitar Drums 90s Fast Weird twee Quirky noise pop Cute playful Drums 80s Fast Weird Sweet Quirky Cute rock pop 90s Fun metal rock Edgy guitar thrash 90s Fierce Weird concert Loud ??? ??? ??? ??? ??? ??? thrash loud metal death thrash rock Edgy gothic heavy metal 90s Weird concert Loud
    12. our proposal
      • propose tags (“wisdom of song crowds”)
      rock Indie Cute guitar Drums 90s Fast Weird twee Quirky noise pop Cute playful Drums 80s Fast Weird Sweet Quirky Cute rock pop 90s Fun metal rock Edgy guitar thrash 90s Fierce Weird concert Loud thrash loud metal death thrash rock Edgy gothic heavy metal 90s Weird concert Loud thrash rock 90s Loud metal Weird
    13. our proposal
      • users validate/add tags
      rock Indie Cute guitar Drums 90s Fast Weird twee Quirky noise pop Cute playful Drums 80s Fast Weird Sweet Quirky Cute rock pop 90s Fun metal rock Edgy guitar thrash 90s Fierce Weird concert Loud thrash loud metal death thrash rock Edgy gothic heavy metal 90s Weird concert Loud thrash rock 90s Loud metal Weird
    14. experiments
      • Ground Truth
        • 5481 annotated songs from Magnatune (John Buckman)
        • 29 Different labels (rock, instrumental, classical, relaxing, etc.)
        • Avg. 3 tags per song
        • Only CB similarity, no user feedback
      • 2 experiments
        • All tags
        • Only mood tags (smaller collection)
    15. experiments:: results
      • Some basic comments...
        • Increasing number of similar songs (10, 20, 30,...)
          • Bad recall, precision
          • Best results: ~10 similars
        • Using album, artist filtering
          • Need to increase the number of similar songs
          • Best results: ~20 similars
      • Metrics
        • Precision / Recall (F-measure, alpha = 2)
        • Spearman Rho
        • (Details on the paper!)
    16. demo
      • Searchsounds, with users
        • ~250,000 full songs
        • ~48% songs annotated, avg. 2 tags per song
          • from CDBaby, Magnatune, etc.
    17.  
    18.  
    19.  
    20.  
    21.  
    22.  
    23.  
    24. conclusions
      • Extent annotations in a music collection
        • General experiment
          • 10 similar, recall >0.4
          • 40%+38% = 78%
        • Mood experiment
          • P@1 = 0.5
          • 30%+35% = 65%
      • Avoid cold start problem (new song)
      • Limitations
        • General experiment: Low precision, due to (valid) proposed tags, that were not in the GT
        • Mood experiment: “Mysterious” label
    25. questions?
      • Preguntes?
      • ¿Preguntas?
      • Questions ?
      • Fragen ?
      • أسئلة ؟
    26. Annotating Music Collections: How Content-Based Similarity Helps to Propagate Labels
        • Mohamed Sordo, Cyril Laurier and Òscar Celma
        • (Music Technology Group, UPF)
    27. experiments:: metrics
      • SongId usana31605
        • Manually annotated (ground truth)
          • Classical, Instrumental, Baroque, Piano
        • Proposed labels (and frequency)
          • Instrumental (0.55), Classical (0.40), Baroque (0.26), Harpsichord (0.24)
        • Precision = TP / (TP + FN)
          • 3 / 4 (added Harpsichord)
        • Recall = TP / (TP + FP)
          • 3 / 4 (Piano is missing)
        • F-measure (alpha = 2 , more weight to Recall)
        • Spearman-Rho
    28. experiments:: results
      • How does CB performs...?
    29. experiments:: results
      • With album, artist filtering...
    30. experiments:: results
      • Recall the recall

    + Oscar CelmaOscar Celma, 2 years ago

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