Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization
| Autor: | Michael Wohlmayr, Franz Pernkopf, Robert Peharz |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
business.industry
Markov process Pitch detection algorithm Pattern recognition Mixture model Matrix decomposition Non-negative matrix factorization symbols.namesake ComputingMethodologies_PATTERNRECOGNITION Computer Science::Sound symbols Artificial intelligence Hidden Markov model business Gaussian process Sparse matrix Mathematics |
| Zdroj: | ICASSP |
| Popis: | While nonnegative matrix factorization (NMF) has successfully been applied for gain-robust multi-pitch detection, a method to track pitch values over time was not provided. We embed NMF-based pitch detection into a recently proposed pitch-tracking system, based on a factorial hidden Markov model (FHMM). The original system models speech spectra with Gaussian mixture models, which is sensitive to a gain mismatch between training and test data. We therefore combine the advantages of these two approaches and derive a gain-adaptive observation model for the FHMM. As training algorithm we use a modification of l0-sparse NMF, which represents the short-time spectrum with scalable basis vectors. In experiments we show that the new approach significantly increases the gain-robustness of the original tracking system. |
| Databáze: | OpenAIRE |
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