2000
DOI: 10.1109/89.841214
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Abstract: Acoustic signals recorded simultaneously in a reverberant environment can be described as sums of differently convolved sources. The task of source separation is to identify the multiple channels and possibly to invert those in order to obtain estimates of the underlying sources. We tackle the problem by explicitly exploiting the nonstationarity of the acoustic sources. Changing cross-correlations at multiple times give a sufficient set of constraints for the unknown channels. A least squares optimization allo… Show more

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Cited by 640 publications
(479 citation statements)
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“…A wide variety of methods have been proposed to address this permutation problem [5][6][7][8]. One interesting approach is to consider the spatial arrangement of the source and microphones: a beamforming approach [17][18][19].…”
Section: Frequency-domain Icamentioning
confidence: 99%
“…A wide variety of methods have been proposed to address this permutation problem [5][6][7][8]. One interesting approach is to consider the spatial arrangement of the source and microphones: a beamforming approach [17][18][19].…”
Section: Frequency-domain Icamentioning
confidence: 99%
“…Sources (Parra) The main idea of the algorithm [7] proposed by L. Parra and C. Spence is similar to the previous ones [2] and [8]. This algorithm will be called later "Parra".…”
Section: Convolutive Blind Separation Of Non-stationarymentioning
confidence: 96%
“…Using these pre-and post-processings, we found that among the tested algorithms, only two [2,7] have given satisfactory results. They were dedicated to separate non-stationary sources (audio or music signals) and will be called in the following SOS [2] and Parra [7]. Both of them are implemented in frequency domain and are using discrete frequency adapted filter.…”
Section: A Background and Assumptionsmentioning
confidence: 99%
“…Many systems have been proposed to separate noise from speech using cues from multiple sensors, e.g. blind source separation by independent component analysis (Parra and Spence, 2000), but separating and recognising speech in single-channel signals, the problem considered in this article, still remains a challenging problem. Human listeners, however, are adept at recognising target speech in such noisy conditions, making use of cues such as pitch continuity, spacial location, and speaking rate (Cooke and Ellis, 2001).…”
Section: Introductionmentioning
confidence: 99%