Second order impropriety based complex-valued algorithm for frequency-domain blind separation of convolutive speech mixtures

The performance of the complex-valued blind source separation (BSS) is studied in the frequency domain approach to separate convolutive speech mixtures. In this context, the strong uncorrelating transform (SUT) and complex maximization of non-Gaussianity (CMN) do not produce satisfactory separation...

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Vydané v:2011 IEEE International Workshop on Machine Learning for Signal Processing s. 1 - 6
Hlavní autori: Fengyu Cong, Qiu-Hua Lin, Peng Jia, Xizhi Shi, Ristaniemi, T.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2011
Predmet:
complex-valued BSS
convolutive speech
Correlation
frequency domain
improper
second order
Source separation
Speech
Time domain analysis
Time frequency analysis
Vectors
ISBN:1457716216, 9781457716218
ISSN:1551-2541
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Popis
Shrnutí:The performance of the complex-valued blind source separation (BSS) is studied in the frequency domain approach to separate convolutive speech mixtures. In this context, the strong uncorrelating transform (SUT) and complex maximization of non-Gaussianity (CMN) do not produce satisfactory separation results since their assumptions about the independence among the frequency-domain complex-valued sources and the different diagonal elements of the pseudo-covariance of those sources are not met at each frequency bin. The proposed strong second order statistics (SSOS) algorithm exploits the second order impropriety of the frequency-domain complex-valued sources with the assumption that the complex-valued sources are improper and uncorrelated, and can well separate the mixtures at about 50% of frequency bins, outperforming SUT and CMN. Thus, it is promising to recover the time-domain speech sources by combing SSOS and the following indeterminacy correction in the frequency domain approach to separate convolutive speech mixtures.
ISBN:1457716216
9781457716218
ISSN:1551-2541
DOI:10.1109/MLSP.2011.6064589