Blind sparse source separation for unknown number of sources using Gaussian mixture model fitting with Dirichlet prior

Shoko Araki, Tomohiro Nakatani, Hiroshi Sawada, Shoji Makino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages33-36
Number of pages4
ISBN (Print)9781424423545
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: 2009 Apr 192009 Apr 24

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
CountryTaiwan, Province of China
CityTaipei
Period09/4/1909/4/24

Keywords

  • Blind source separation
  • Dirichlet distribution
  • Number of sources
  • Prior
  • Sparse

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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