Abstract
Based on sparse representation, this paper discusses convolutive BSS of sparse sources and presents a FIR convolutive BSS algorithm that works in the frequency domain. This algorithm does not require that source signals be i.i.d or stationary, but require that source signals be sufficiently sparse in frequency domain. Furthermore, our algorithm can overcome permutation problem of frequency convolutive BSS methods. For short-order FIR convolution, simulation shows good performance of our algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Parra, L., Spence, C.: Convolutive Blind Separation of Nonstationary Sources. IEEE Trans. Speech Audio Processing 8, 320–327 (2000)
Lee, T.W., Lewicki, M.S., et al.: Blind Source Separation of More Sources than Mixtures Using Overcomplete Representations. IEEE Signal Processing Letters 6, 87–90 (1999)
Tan, L., Wei, G.: Multi-input Multi-output(MIMO) Blind Deconvolution via Maximum Entropy(ME) Method. Acta Electronica Sinica 28, 114–116 (2000)
Yellin, D., Weinstein, E.: Criteria for Multichannel Signal Separation. IEEE Trans. Signal Processing 42, 2158–2168 (1994)
Thi, H.N., Jutten, C.: Blind Source Separation for Convolutive Mixtures. Signal Processing 45, 209–229 (1995)
Liu, J., He, Z.: A Survey of Blind Source Separation and Blind Deconvolution. Acta Electronica Sinica 30, 570–576 (2002)
Tokkola, K.: Blind Separation of Delayed Sources Based on Information Maximization. In: Proc of ICASSP, Atlanta, pp. 3509–3512 (1996)
Li, Y., Cichocki, A., Amari, S.: Analysis of Sparse Representation and Blind Source Separation. Neural Computation 16, 1193–1234 (2004)
Bofill, P., Zibulevsky, M.: Underdetermined Source Separation Using Sparse Representations. Signal Processing 81, 2353–2362 (2001)
Zibulevsky, M., Pearlmutter, B.A.: Blind Source Separation by Sparse Decomposition in a Signal Dictionary. Neural Computation 13, 863–882 (2001)
Donoho, D.L., Elad, M.: Maximal Sparsity Representation via l 1 Minimization. Proc. Nat. Aca, Sci. 100, 2197–2202 (2003)
Adriana, D., Luis, C.: A Novel Frequency Domain Approach for Separating Convolutive Mixtures of Temporally-White Signals. Digital Signal Processing 13, 301–316 (2003)
Lewicki, M.S., Sejnowski, T.J.: Learning Overcomplete Representations. Neural Computation 12, 337–365 (2000)
Takigawa, I., Kudo, M., Toyama, J.: Performance Analysis of Minimum 1 l -norm Solutions for Underdetermined Source Separation. IEEE Trans. Signal Processing 52, 582–591 (2004)
He, Z., Xie, S., Deng, A., Tan, B.: An Adaptive BSS Algorithm for Undetermined Blind Source Separation. Submitted to IEEE Trans. Signal Processing
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, Z., Xie, S., Fu, Y. (2005). FIR Convolutive BSS Based on Sparse Representation. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_87
Download citation
DOI: https://doi.org/10.1007/11427445_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)