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UNDERDETERMINED BLIND SEPARATION AND TRACKING OF MOVING SOURCES BASED ON DOA-HMM
| Content Provider | CiteSeerX |
|---|---|
| Author | Higuchiy, Takuya Takamuney, Norihiro Nakamuray, Tomohiko Kameokayz, Hirokazu |
| Abstract | This paper deals with the problem of the underdetermined blind separation and tracking of moving sources. In practical situation-s, 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 prob-lem through the posterior inference of the parameters in a generative model of an observed multichannel signal, formulated under the as-sumption of the sparsity of time-frequency components of speech and the continuity of speakers ’ movements. Specifically, we de-scribe a generative model of mixture signals by incorporating a gen-erative model of a time-varying frequency array response for each source, described using a path-restricted hidden Markov model (H-MM). Each hidden state of the present HMM represents the direc-tion 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 improve-ment compared with the conventional method in terms of the signal-to-interference ratio. Index Terms — Underdetermined blind separation, moving sources, direction of arrival, hidden Markov model, variational inference 1. |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
