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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Vydáno v:2011 IEEE International Workshop on Machine Learning for Signal Processing s. 1 - 6
Hlavní autoři: Fengyu Cong, Qiu-Hua Lin, Peng Jia, Xizhi Shi, Ristaniemi, T.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2011
Témata:
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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Abstract 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.
AbstractList 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.
Author Qiu-Hua Lin
Peng Jia
Ristaniemi, T.
Fengyu Cong
Xizhi Shi
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  surname: Fengyu Cong
  fullname: Fengyu Cong
  organization: Dept. of Math. Inf. Technol., Univ. of Jyvaskyla, Jyvaskyla, Finland
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  surname: Qiu-Hua Lin
  fullname: Qiu-Hua Lin
  organization: Sch. of Inf. & Commun. Eng., Dalian Univ. of Technol., Dalian, China
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  surname: Peng Jia
  fullname: Peng Jia
  organization: NERC for Mobile Satellite Commun., Nanjing, China
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  surname: Xizhi Shi
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  organization: Mech. Eng. Sch., Shanghai Jiao Tong Univ., Shanghai, China
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  givenname: T.
  surname: Ristaniemi
  fullname: Ristaniemi, T.
  organization: Dept. of Math. Inf. Technol., Univ. of Jyvaskyla, Jyvaskyla, Finland
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Snippet The performance of the complex-valued blind source separation (BSS) is studied in the frequency domain approach to separate convolutive speech mixtures. In...
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SubjectTerms complex-valued BSS
convolutive speech
Correlation
frequency domain
improper
second order
Source separation
Speech
Time domain analysis
Time frequency analysis
Vectors
Title Second order impropriety based complex-valued algorithm for frequency-domain blind separation of convolutive speech mixtures
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