TY - GEN
T1 - Non-negative hidden Markov modeling of audio with application to source separation
AU - Mysore, Gautham J.
AU - Smaragdis, Paris
AU - Raj, Bhiksha
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=78349237022&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78349237022&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15995-4_18
DO - 10.1007/978-3-642-15995-4_18
M3 - Conference contribution
AN - SCOPUS:78349237022
SN - 364215994X
SN - 9783642159947
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 140
EP - 148
BT - Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings
T2 - 9th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2010
Y2 - 27 September 2010 through 30 September 2010
ER -