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Locating Singing Voice Segments Within Music Signals

Locating Singing Voice Segments Within Music Signals. Adam Berenzweig and Daniel P.W. Ellis LabROSA, Columbia University alb63@columbia.edu, dpwe@ee.columbia.edu. LabROSA. What Where Who Why you love us. The Future as We Hear It. Online Digital Music Libraries

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Locating Singing Voice Segments Within Music Signals

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  1. Locating Singing Voice Segments Within Music Signals Adam Berenzweig and Daniel P.W. Ellis LabROSA, Columbia University alb63@columbia.edu, dpwe@ee.columbia.edu

  2. LabROSA • What • Where • Who • Why you love us

  3. The Future as We Hear It • Online Digital Music Libraries • The Coming Age of Streaming Music Services • Information Retrieval: How do we find what we want? • Recommendation: How do we know what we want to find? • Collaborative Filtering vs. Content-Based • What is Quality?

  4. Motivation • Lyrics Recognition: Baby Steps • Segmentation • Forced Alignment • A Corpus • Song structure through singing structure? • Fingerprinting • Retreival • Feature for similarity measures

  5. Lyrics Recognition: Can YOU do it? • Notoriously hard, even for humans. • amIright.com, kissThisGuy.com • Why so hard? • Noise, music, whatever. • Singing is not speech: voice transformations • Strange word sequences (“poetry”) • Need a corpus

  6. History of the Problem • Segmentation for Speech Recognition: Music/Speech • Scheirer & Slaney • Forced Alignment - Karaoke • Cano et al. [REF NEEDED] • Acoustic feature design: Custom job or Kitchen Sink? • Idea! Use a speech recognizer: PPF (Posterior Probability Features) • Williams & Ellis • Ultimately: Source separation, CASA

  7. A Peek at the End

  8. Architecture Overview • Entropy H • H/h# • Dynamism D • P(h#) cepstra posteriogram Audio PLP Speech Recognizer (Neural Net) Feature Calculation Time- averaging Segmentation (HMM) Gaussian Model Gaussian Model

  9. Architecture Overview cepstra posteriogram Audio PLP Speech Recognizer (Neural Net) Neural Net Segmentation (HMM) Neural Net

  10. “So how’s that working out for you, being clever?” • Entropy • Entropy excluding background • Dynamism • Background probability • Distribution Match: Likelihoods under single Gaussian model • Cepstra • PPF

  11. Recovering context with the HMM • Transition probabilities • Inverse average segment duration • Emission probabilities • Gaussian fit to time-averaged distribution • Segmentation: the Viterbi path • Evaluation • Frame error rate (no boundary consideration)

  12. Results • [Table, figures] • Listen! • Good, bad • trigger & stick • genre effects?

  13. Results

  14. E = .075 • P(h#) in effect

  15. E = .68 • P(h#) gone bad

  16. ‘m’,’n’ ‘uw’ ‘ey’ • E = .61 • Strong phones trigger, but can’t hold it • Production quality effect?

  17. ‘s’ • E = .25 • “Trigger and Stick”

  18. ‘bcl’,’dcl’,’b’, ‘d’ ‘l’,’r’ • E = .54 • False phones

  19. E = .20 • Genre effect?

  20. Discussion • The Moral of the Story: Just give it the data • PPF is better than cepstra. Speech Recognizer is pretty powerful. • Why does the extra Gaussian model help PPF but not cepstra? • Time averaging helps PPF: proves that it’s using the overall distribution, not short-time detail (at least, when modelled by single gaussians)

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