Abstract
Underdetermined source separation is often carried out by modeling time-frequency source coefficients via a fixed sparse prior. This approach fails when the number of active sources in one time-frequency bin is larger than the number of channels or when active sources lie on both sides of an inactive source. In this article, we partially address these issues by modeling time-frequency source coefficients via Gaussian priors with free variances. We study the resulting maximum likelihood criterion and derive a fast non-iterative optimization algorithm that finds the global minimum. We show that this algorithm outperforms state-of-the-art approaches over stereo instantaneous speech mixtures.
Keywords
- Global Minimum
- Nonzero Entry
- Active Source
- Source Separation
- Blind Source Separation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your 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
Zibulevsky, M., Pearlmutter, B.A., Bofill, P., Kisilev, P.: Blind source separation by sparse decomposition in a signal dictionary. In: Independent Component Analysis: Principles and Practice, pp. 181–208. Cambridge Press (2001)
Davies, M.E., Mitianoudis, N.: Simple mixture model for sparse overcomplete ICA. IEE Proceedings on Vision, Image and Signal Processing 151(1), 35–43 (2004)
Vincent, E.: Complex nonconvex l p norm minimization for underdetermined source separation. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 430–437. Springer, Heidelberg (2007)
Xiao, M., Xie, S., Fu, Y.: A statistically sparse decomposition principle for underdetermined blind source separation. In: Proc. Int. Symp. on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 165–168 (2005)
Belouchrani, A., Amin, M.G., Abed-Meraïm, K.: Blind source separation based on time-frequency signal representations. IEEE Trans. on Signal Processing 46(11), 2888–2897 (1998)
Arberet, S., Gribonval, R., Bimbot, F.: A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 536–543. Springer, Heidelberg (2006)
Pham, D.T., Cardoso, J.F.: Blind separation of instantaneous mixtures of non stationary sources. IEEE Trans. on Signal Processing 49(9), 1837–1848 (2001)
Vincent, E., Gribonval, R., Plumbley, M.D.: Oracle estimators for the benchmarking of source separation algorithms. Signal Processing 87(8), 1933–1950 (2007)
Pulkki, V., Karjalainen, M.: Localization of amplitude-panned virtual sources I: stereophonic panning. Journal of the Audio Engineering Society 49(9), 739–752 (2001)
Vincent, E., Gribonval, R., Févotte, C.: Performance measurement in blind audio source separation. IEEE Trans. on Audio, Speech and Language Processing 14(4), 1462–1469 (2006)
Yılmaz, O., Rickard, S.T.: Blind separation of speech mixtures via time-frequency masking. IEEE Trans. on Signal Processing 52(7), 1830–1847 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vincent, E., Arberet, S., Gribonval, R. (2009). Underdetermined Instantaneous Audio Source Separation via Local Gaussian Modeling. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_97
Download citation
DOI: https://doi.org/10.1007/978-3-642-00599-2_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00598-5
Online ISBN: 978-3-642-00599-2
eBook Packages: Computer ScienceComputer Science (R0)