TY - GEN
T1 - Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior
AU - Araki, Shoko
AU - Nakatani, Tomohiro
AU - Sawada, Hiroshi
AU - Makino, Shoji
PY - 2009
Y1 - 2009
N2 - In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches with time-frequency masks have been proposed. However, most of these approaches require information on the number of sources in advance. In our proposed method, we model the histogram of the estimated direction of arrival (DOA) with a Gaussian mixture model (GMM) with a Dirichlet prior. Then we estimate the model parameters by using the maximum a posteriori estimation based on the EM algorithm. In order to avoid one cluster being modeled by two or more Gaussians, we utilize a sparse distribution modeled by the Dirichlet distributions as the prior of the GMM mixture weight. By using this prior, without any specific model selection process, our proposed method can estimate the number of sources and time-frequency masks simultaneously. Experimental results show the performance of our proposed method.
AB - In this paper, we propose a novel sparse source separation method that can be applied even if the number of sources is unknown. Recently, many sparse source separation approaches with time-frequency masks have been proposed. However, most of these approaches require information on the number of sources in advance. In our proposed method, we model the histogram of the estimated direction of arrival (DOA) with a Gaussian mixture model (GMM) with a Dirichlet prior. Then we estimate the model parameters by using the maximum a posteriori estimation based on the EM algorithm. In order to avoid one cluster being modeled by two or more Gaussians, we utilize a sparse distribution modeled by the Dirichlet distributions as the prior of the GMM mixture weight. By using this prior, without any specific model selection process, our proposed method can estimate the number of sources and time-frequency masks simultaneously. Experimental results show the performance of our proposed method.
KW - Blind source separation
KW - Dirichlet distribution
KW - Number of sources
KW - Prior
KW - Sparse
UR - http://www.scopus.com/inward/record.url?scp=70349466745&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349466745&partnerID=8YFLogxK
U2 - 10.1109/icassp.2009.4959513
DO - 10.1109/icassp.2009.4959513
M3 - Conference contribution
AN - SCOPUS:70349466745
SN - 9781424423545
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 33
EP - 36
BT - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Y2 - 19 April 2009 through 24 April 2009
ER -