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2010
Springer

Non-negative Hidden Markov Modeling of Audio with Application to Source Separation

10 years 5 months ago
Non-negative Hidden Markov Modeling of Audio with Application to Source Separation
Abstract. 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.
Gautham J. Mysore, Paris Smaragdis, Bhiksha Raj
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where ICA
Authors Gautham J. Mysore, Paris Smaragdis, Bhiksha Raj
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