Sundar H, Sreenivas TV, Kellermann W (2012)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Book Volume: 20
Pages Range: 153-156
Article Number: 6392861
Journal Issue: 2
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. © 1994-2012 IEEE.
APA:
Sundar, H., Sreenivas, T.V., & Kellermann, W. (2012). Identification of active sources in single-channel convolutive mixtures using known source models. IEEE Signal Processing Letters, 20(2), 153-156. https://doi.org/10.1109/LSP.2012.2236314
MLA:
Sundar, Harshavardhan, Thippur V. Sreenivas, and Walter Kellermann. "Identification of active sources in single-channel convolutive mixtures using known source models." IEEE Signal Processing Letters 20.2 (2012): 153-156.
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