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

TitleNon-negative Hidden Markov Modeling of Audio with Application to Source Separation
Publication TypeConference Paper
Year of Publication2010
AuthorsMysore, G. J., P. Smaragdis, and B. Raj
Conference NameInternational Conference on Latent Variable Analysis and Signal Separation (LVA / ICA)
Date Published09/2010
Conference LocationSt. Malo, France
AbstractIn 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.
URLhttps://ccrma.stanford.edu/~gautham/Site/NFHMM.html
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