Blind source separation exploiting higher-order frequency dependencies

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Blind source separation (BSS) is a challenging problem in real-world environments where sources are time delayed and convolved. The problem becomes more difficult in very reverberant conditions, with an increasing number of sources, and geometric configurations of the sources such that finding directionality is not sufficient for source separation. In this paper, we propose a new algorithm that exploits higher order frequency dependencies of source signals in order to separate them when they are mixed. In the frequency domain, this formulation assumes that dependencies exist between frequency bins instead of defining independence for each frequency bin. In this manner, we can avoid the well-known frequency permutation problem. To derive the learning algorithm, we define a cost function, which is an extension of mutual information between multivariate random variables. By introducing a source prior that models the inherent frequency dependencies, we obtain a simple form of a multivariate score function. In experiments, we generate simulated data with various kinds of sources in various environments. We evaluate the performances and compare it with other well-known algorithms. The results show the proposed algorithm outperforms the others in most cases. The algorithm is also able to accurately recover six sources with six microphones. In this case, we can obtain about 16-dB signal-to-interference ratio (SIR) improvement. Similar performance is observed in real conference room recordings with three human speakers reading sentences and one loudspeaker playing music.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2007-01
Language
English
Article Type
Article
Keywords

ICA MIXTURE-MODELS; NATURAL IMAGES; CLASSIFICATION; DOMAIN

Citation

IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, v.15, pp.70 - 79

ISSN
1558-7916
DOI
10.1109/TASL.2006.872618
URI
http://hdl.handle.net/10203/10202
Appears in Collection
EE-Journal Papers(저널논문)
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