Blind separation of nonstationary sources based on spatial time-frequency distributions.

Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution (TFD). To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.

Main Author: Zhang, Yimin.
Other Authors: Amin, Moeness G.
Language: English
Published: 2006
Online Access: http://ezproxy.villanova.edu/login?url=https://digital.library.villanova.edu/Item/vudl:173183
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dc_source_str_mv EURASIP Journal on Applied Signal Processing, vol. 2006, article ID 64785, 13 pages, 2006.
author Zhang, Yimin.
author_facet_str_mv Zhang, Yimin.
Amin, Moeness G.
author_or_contributor_facet_str_mv Zhang, Yimin.
Amin, Moeness G.
author_s Zhang, Yimin.
spellingShingle Zhang, Yimin.
Blind separation of nonstationary sources based on spatial time-frequency distributions.
author-letter Zhang, Yimin.
author_sort_str Zhang, Yimin.
author2 Amin, Moeness G.
author2Str Amin, Moeness G.
dc_title_str Blind separation of nonstationary sources based on spatial time-frequency distributions.
title Blind separation of nonstationary sources based on spatial time-frequency distributions.
title_short Blind separation of nonstationary sources based on spatial time-frequency distributions.
title_full Blind separation of nonstationary sources based on spatial time-frequency distributions.
title_fullStr Blind separation of nonstationary sources based on spatial time-frequency distributions.
title_full_unstemmed Blind separation of nonstationary sources based on spatial time-frequency distributions.
collection_title_sort_str blind separation of nonstationary sources based on spatial time-frequency distributions.
title_sort blind separation of nonstationary sources based on spatial time-frequency distributions.
description Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution (TFD). To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.
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fgs.label Blind separation of nonstationary sources based on spatial time-frequency distributions.
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dc.title Blind separation of nonstationary sources based on spatial time-frequency distributions.
dc.creator Zhang, Yimin.
Amin, Moeness G.
dc.description Blind source separation (BSS) based on spatial time-frequency distributions (STFDs) provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f) domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution (TFD). To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.
dc.date 2006
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dc.source EURASIP Journal on Applied Signal Processing, vol. 2006, article ID 64785, 13 pages, 2006.
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