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Finding a single voice in music

Finding a single voice in music. Christine Smit April 26, 2007. Outline. Introduction Classification Strategies: Counting silent frequency bins Pitch cancellation MFCCs Trading recall for precision What worked and what didn’t. Introduction. What am I doing?. What is a ‘single voice’?.

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Finding a single voice in music

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  1. Finding a single voice in music Christine Smit April 26, 2007

  2. Outline • Introduction • Classification Strategies: • Counting silent frequency bins • Pitch cancellation • MFCCs • Trading recall for precision • What worked and what didn’t

  3. Introduction What am I doing?

  4. What is a ‘single voice’? • a single note sounding at a time

  5. Why do this? single voice finder + instrument identifier = instrument sample library

  6. What are the data sets? • training set: 10 1-minute samples • test set: 10 1-minute test samples • 25% single voice, 75% multi-voice/silence • mixture of classical and folk music

  7. What characterizes a single voice? non-solo solo non-solo

  8. What characterizes a single voice?

  9. What characterizes a single voice?

  10. Strategies

  11. Strategy #1: Silence detection music find silence silence counts silent raw classification HMM? Nothing really worked

  12. Strategy #2: Pitch Cancellation music filter pitch filtered music single voice? raw classification HMM final classification

  13. Strategy #3: MFCCs music MFCC 13 features GMM likelihood HMM final classification

  14. Trading recall for precision

  15. Quick reminder • Precision = out of the stuff we got, how much of it was right? Are google’s results relevant? • Recall = out of all the right stuff, how much did we get? If I asked google for the UN, did I get all the UN’s websites?

  16. Precision is important • If I have a large enough database, I can afford to have relatively low recall. But I want high precision so what I do get is what I want.

  17. Strategy #2: Pitch Cancellation music filter pitch Tweak Cutoff filtered music single voice? raw classification HMM final classification

  18. Strategy #3: MFCCs music MFCC 13 features GMM Tweak Probabilities likelihood HMM final classification

  19. Results

  20. Strategy #1: Silence detection (just for comparison)

  21. Strategy #2: Pitch Cancellation

  22. Strategy #3: MFCCs

  23. Conclusion • Silence detection really didn’t work out. • MFCCs + GMM is really just as good as pitch cancellation • At 90% precision, I get about 25% recall.

  24. Acknowledgements Much thanks to Professor Ellis for his assistance on this project.

  25. Questions?

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