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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Hyvarinen, A., Karhnen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Chichester (2001)
Makeig, S., Jung, T., Bell, A.J., Ggahremani, D., Seijnowski, T.J.: Blind separation of auditory event-related brain response into independent components. In: Proceedings on National Academic Science, USA, pp. 10979–10984 (1997)
Araki, S., Makino, S., Nishikawa, T., Saruwatari, H.: Fundamental limitation of frequency domain blind separation for convolutive mixture of speech. In: Proc. ICASSP 2001, pp. 2737–2740 (2001)
Ikram, M.Z., Morgan, D.R.: Exploring permutaion inconsistency in blind separation of speech signals in a reverberant environment. In: Proc. of ICASSP 2000, pp. 1041–1044 (2000)
Mukai, R., Araki, S., Makino, S.: Separation and dereverbration performance of frequency domain blind source separation. In: Proc. of Int. Conf. on Independent Component Analysis and Blind Signal Separation (ICA 2001), pp. 230–235 (2001)
Mukai, R., Araki, S., Sawada, H., Makino, S.: Removal of residual crosstalk components in blind source separation using LMS filters. In: Proc. ICASSP 2002, pp. 435–444 (2002)
Low, S.Y., Nordholm, S., Togneri, R.: Convolutive blind signal separation with post-processing. IEEE Trans. on speech and audio processing 12(5) (2004)
Araki, S., Blin, A., Mukai, R., Sawada, H., Makino, S.: Underdetermined blind separation for speech in real environments with sparseness and ICA. In: Proc. ICASSP 2004, vol. 3(17-21), pp. 881–884 (2004)
Amari, S.I., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: Advances in Neural Information Processing Systems, vol. 8. MIT Press, Cambridge (1996)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41, 1–24 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, H., Park, J., Park, K. (2006). Post-processing for Enhancing Target Signal in Frequency Domain Blind Source Separation. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_64
Download citation
DOI: https://doi.org/10.1007/11848035_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
eBook Packages: Computer ScienceComputer Science (R0)