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Published in:
2010 | OriginalPaper | Chapter
Non-negative Hidden Markov Modeling of Audio with Application to Source Separation
Authors : Gautham J. Mysore, Paris Smaragdis, Bhiksha Raj
Published in: Latent Variable Analysis and Signal Separation
Publisher: Springer Berlin Heidelberg
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In recent years, there has been a great deal of work in modeling audio using non-negative matrix factorization and its probabilistic counterparts as they yield rich models that are very useful for source separation and automatic music transcription. Given a sound source, these algorithms learn a dictionary of spectral vectors to best explain it. This dictionary is however learned in a manner that disregards a very important aspect of sound, its temporal structure. We propose a novel algorithm, the non-negative hidden Markov model (N-HMM), that extends the aforementioned models by jointly learning several small spectral dictionaries as well as a Markov chain that describes the structure of changes between these dictionaries. We also extend this algorithm to the non-negative factorial hidden Markov model (N-FHMM) to model sound mixtures, and demonstrate that it yields superior performance in single channel source separation tasks.