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Identification of Active Sources in Single-Channel Convolutive Mixtures Using Known Source Models

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3 Author(s)
Sundar, H. ; Dept. of Electr. Commun. Eng., Indian Inst. of Sciencece, Bangalore, India ; Sreenivas, T.V. ; Kellermann, W.

We address the problem of identifying the constituent sources in a single-sensor mixture signal consisting of contributions from multiple simultaneously active sources. We propose a generic framework for mixture signal analysis based on a latent variable approach. The basic idea of the approach is to detect known sources represented as stochastic models, in a single-channel mixture signal without performing signal separation. A given mixture signal is modeled as a convex combination of known source models and the weights of the models are estimated using the mixture signal. We show experimentally that these weights indicate the presence/absence of the respective sources. The performance of the proposed approach is illustrated through mixture speech data in a reverberant enclosure. For the task of identifying the constituent speakers using data from a single microphone, the proposed approach is able to identify the dominant source with up to 8 simultaneously active background sources in a room with RT60= 250 ms, using models obtained from clean speech data for a Source to Interference Ratio (SIR) greater than 2 dB.

Published in:

Signal Processing Letters, IEEE  (Volume:20 ,  Issue: 2 )