Abstract
We consider the local Gaussian modeling framework for under-determined convolutive audio source separation, where the spatial image of each source is modeled as a zero-mean Gaussian variable with full-rank time- and frequency-dependent covariance. We investigate two methods to improve the accuracy of parameter estimation, based on the use of local observed covariance and auditory-motivated time-frequency representation. We derive an iterative expectation-maximization (EM) algorithm with a suitable initialization scheme. Experimental results over stereo synthetic reverberant mixtures of speech show the effectiveness of the proposed methods.
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
Yılmaz, O., Rickard, S.T.: Blind separation of speech mixtures via time-frequency masking. IEEE Trans. on Signal Processing 52(7), 1830–1847 (2004)
Vincent, E., Jafari, M.G., Abdallah, S.A., Plumbley, M.D., Davies, M.E.: Probabilistic modeling paradigms for audio source separation. In: Wang, W. (ed.) Machine Audition: Principles, Algorithms and Systems. GI Global (to appear)
Duong, N.Q.K., Vincent, E., Gribonval, R.: Under-determined reverberant audio source separation using a full-rank spatial covariance model. IEEE Trans. on Audio, Speech and Language Processing (2010) (to appear)
Vincent, E., Arberet, S., Gribonval, R.: Underdetermined instantaneous audio source separation via local Gaussian modeling. In: Proc. ICA, pp. 775–782 (2009)
Févotte, C., Cardoso, J.F.: Maximum likelihood approach for blind audio source separation using time-frequency Gaussian models. In: Proc. WASPAA, pp. 78–81 (2005)
Deville, Y.: Temporal and time-frequency correlation-based blind source separation methods. In: Proc. ICA, pp. 1059–1064 (2003)
Roman, N., Wang, D., Brown, G.: Speech segregation based on sound localization. Journal of the ASA 114(4), 2236–2252 (2003)
Vincent, E.: Musical source separation using time-frequency source priors. IEEE Trans. on Audio, Speech and Language Processing 14 (1), 91–98 (2006)
Burred, J., Sikora, T.: Comparison of frequency-warped representations for source separation of stereo mixtures. In: Proc. 121st AES Convention (October 2006)
Winter, S., Kellermann, W., Sawada, H., Makino, S.: MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and ℓ1-norm minimization. EURASIP Journal on Advances in Signal Processing,, 2007, article ID 24717 (2007)
Sawada, H., Araki, S., Mukai, R., Makino, S.: Grouping separated frequency components by estimating propagation model parameters in frequency-domain blind source separation. IEEE Trans. on Audio, Speech, and Language Processing 15(5), 1592–1604 (2007)
Vincent, E., Sawada, H., Bofill, P., Makino, S., Rosca, J.: First stereo audio source separation evaluation campaign: data, algorithms and results. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 552–559. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duong, N.Q.K., Vincent, E., Gribonval, R. (2010). Under-Determined Reverberant Audio Source Separation Using Local Observed Covariance and Auditory-Motivated Time-Frequency Representation. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-15995-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
Online ISBN: 978-3-642-15995-4
eBook Packages: Computer ScienceComputer Science (R0)