Post-processing for enhancing target signal in frequency domain blind source separation

Authors:
Hyuntae Kim;Jangsik Park;Keunsoo Park
Affiliations:
Department of Multimedia Engineering, Dongeui University, Busan, Korea;Department of Digital Inform. Electronic Engineering, Dongeui Institute of Tech, Busan, Korea;Department of Electronic Engineering, Pusan National University, Busan, Korea
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

Citing 2
Cited 0

Exploring permutation inconsistency in blind separation of speech signals in a reverberant environment

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Fundamental limitation of frequency domain blind source separation for convolutive mixture of speech

ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05

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Abstract

The performance of blind source separation (BSS) using independent component analysis (ICA) declines significantly in a reverberant environment. The degradation is mainly caused by the residual crosstalk components derived from the reverberation of the interference signal. A post-processing method is proposed in this paper which uses a approximated Wiener filter using short-time magnitude spectra in the spectral domain. The speech signals have a sparse characteristic in the spectral domain, hence the approximated Wiener filtering can be applied by endowing the difference weights to the other signal components. The results of the experiments show that the proposed method improves the noise reduction ratio(NRR) by about 3dB over conventional FDICA. In addition, the proposed method is compared to the other post-processing algorithm using NLMS algorithm for post-processor [6], and show the better performances of the proposed method.