Abstract
We address the issue of source separation in a particular informed configuration where both the sources and the mixtures are assumed to be known during a so-called encoding stage. This knowledge enables the computation of a side information which ought to be small enough to be watermarked in the mixtures. At the decoding stage, the sources are no longer assumed to be known, only the mixtures and the side information are processed to perform source separation.
The proposed method models the sources jointly using latent variables in a framework close to multichannel nonnegative matrix factorization and models the mixing process as linear filtering. Separation at the decoding stage is done using generalized Wiener filtering of the mixtures. An experimental setup shows that the method gives very satisfying results with mixtures composed of many sources. A study of its performance with respect to the number of latent variables is presented.
This work is supported by the French National Research Agency (ANR) as a part of the DReaM project (ANR-09-CORD-006-03) and partly supported by the Quaero Program, funded by OSEO.
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.
Similar content being viewed by others
References
Barchiesi, D., Reiss, J.: Automatic target mixing using least-squares optimization of gains and equalization settings. In: Proc. of the 12th Conf. on Digital Audio Effects (DAFx 2009), Como, Italy, September 2009, pp. 7–14 (2009)
Benaroya, L., Bimbot, F., Gribonval, R.: Audio source separation with a single sensor. IEEE Trans. on Audio, Speech and Language Processing 14(1), 191–199 (2006)
Bertin, N., Févotte, C., Badeau, R.: A tempering approach for Itakura-Saito non-negative matrix factorization. With application to music transcription. In: Proc. IEEE Intl. Conf. Acoust. Speech Signal Processing (ICASSP 2009), Washington, DC, USA, April 2009, pp. 1545–1548 (2009)
Févotte, C., Bertin, N., Durrieu, J.-L.: Nonnegative matrix factorization with the Itakura-Saito divergence with application to music analysis. Neural Computation 21(3), 793–830 (2009)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems 13, 556–562 (2001)
Ozerov, A., Févotte, C.: Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Trans. on Audio, Speech and Language Processing 18(3), 550–563 (2010)
Parvaix, M., Girin, L., Brossier, J.-M.: A watermarking-based method for informed source separation of audio signals with a single sensor. IEEE Transactions on Audio, Speech and Language Processing (2010) (to be published)
Vincent, E., Févotte, C., Gribonval, R.: Performance measurement in blind audio source separation. IEEE Trans. on Audio, Speech and Language Processing 14(4), 1462–1469 (2006)
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
Liutkus, A., Badeau, R., Richard, G. (2010). Informed Source Separation Using Latent Components. 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_62
Download citation
DOI: https://doi.org/10.1007/978-3-642-15995-4_62
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)