Identification of active sources in single-channel convolutive mixtures using known source models

Journal article
(Original article)


Publication Details

Author(s): Sundar H, Sreenivas TV, Kellermann W
Journal: IEEE Signal Processing Letters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2013
Volume: 20
Journal issue: 2
Pages range: 153-156
ISSN: 1070-9908


Abstract


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.



FAU Authors / FAU Editors

Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Sundar, H., Sreenivas, T.V., & Kellermann, W. (2013). Identification of active sources in single-channel convolutive mixtures using known source models. IEEE Signal Processing Letters, 20(2), 153-156. https://dx.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 (2013): 153-156.

BibTeX: 

Last updated on 2018-19-04 at 03:34