NDLI: Underdetermined blind separation and tracking of moving sources based ONDOA-HMM

Content Provider	IEEE Xplore Digital Library
Author	Higuchi, T. Takamune, N. Nakamura, T. Kameoka, H.
Copyright Year	2014
Description	Author affiliation: Grad. Sch. of Inf. Sci. & Technol., Univ. of Tokyo, Tokyo, Japan (Higuchi, T.; Takamune, N.; Nakamura, T.; Kameoka, H.)
Abstract	This paper deals with the problem of the underdetermined blind separation and tracking of moving sources. In practical situations, sound sources such as human speakers can move freely and so blind separation algorithms must be designed to track the temporal changes of the impulse responses. We propose solving this problem through the posterior inference of the parameters in a generative model of an observed multichannel signal, formulated under the assumption of the sparsity of time-frequency components of speech and the continuity of speakers' movements. Specifically, we describe a generative model of mixture signals by incorporating a generative model of a time-varying frequency array response for each source, described using a path-restricted hidden Markov model (HMM). Each hidden state of the present HMM represents the direction of arrival (DOA) of each source, and so we call it a “DOA-HMM.” Through the posterior inference of the overall generative model, we can simultaneously track the DOAs of sources, separate source signals and perform permutation alignment. The experiment showed that the proposed algorithm provided a 6.20 dB improvement compared with the conventional method in terms of the signal-to-interference ratio.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	3191
Ending Page	3195
File Size	329910
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854189
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Time-frequency analysis Arrays Source separation Speech Direction-of-arrival estimation Microphones variational inference Underdetermined blind separation moving sources direction of arrival hidden Markov model
Content Type	Text
Resource Type	Article

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