Abstract
Source sparsity is a common assumption in many solutions proposed in literature to the problem of blind source separation with more sources than mixtures. As shown in this work, representation of signals in different wavelet domains can be efficiently applied in order to get improved sparsity. Moreover, the approach here presented allows to directly perform a de-noising operation after the separation algorithm, at a very low computational cost, resulting in a further improvement of source recovering when noise is present at mixture level. Experimental results confirm the effectiveness of developed idea.
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References
Bofill, P., Zibulevsky, M.: Underdetermined blind source separation using sparse representation. Signal Processing 81(1), 2353–2362 (2000)
Jourjine, A., Rickard, S., Yilmaz, O.: Blind separation of disjoint orthogonal signals: demixing N sources from 2 mixtures. In: Proc. ICASSP 2000, pp. 2985–2988 (2000)
Casey, M.A.: Separation of mixed audio sources by independent subspace analysis. In: Proc. International Computer Music Conference (2000)
Kisilev, P., Zibulevsky, M., Zeevi, Y., Pearlmutter, B.A.: Blind source separation via multimode sparse representation. Advances in Neural Information Processing Systems, 1049–1056 (2002)
Theis, F.J., Lang, E.W.: Formalization of the two-step approach to overcomplete BSS. SIP (2002)
Lewicki, M.S., Sejnowski, T.J.: Learning overcomplete representation. Neural Computation (1988)
Vetterli, M., Kovacevic, J.: Wavelet and subband coding. Prentice-Hall, Englewood Cliffs (1995)
Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., Picard, D.: Wavelet shrinkage: asymptopia. Jour. Roy. Stat. Soc., series B 57, 301–369 (1995)
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© 2005 Springer-Verlag Berlin Heidelberg
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Pomponi, E., Squartini, S., Piazza, F. (2005). Signal Sparsity Enhancement Through Wavelet Transforms in Underdetermined BSS. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_22
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DOI: https://doi.org/10.1007/11520153_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27441-4
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