Abstract
In this paper, we propose a novel sparse source separation method that can estimate the number of sources and time-frequency masks simultaneously, even when the spatial aliasing problem exists. Recently, many sparse source separation approaches with time-frequency masks have been proposed. However, most of these approaches require information on the number of sources in advance. In our proposed method, we model the phase difference of arrival (PDOA) between microphones with a Gaussian mixture model (GMM) with a Dirichlet prior. Then we estimate the model parameters by using the maximum a posteriori (MAP) estimation based on the EM algorithm. In order to avoid one cluster being modeled by two or more Gaussians, we utilize a sparse distribution modeled by the Dirichlet distributions as the prior of the GMM mixture weight. Moreover, to handle wide microphone spacing cases where the spatial aliasing problem occurs, the indeterminacy of modulus 2πk in the phase is also included in our model. Experimental results show good performance of our proposed method.
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
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Chichester (2001)
Yilmaz, O., Rickard, S.: Blind separation of speech mixtures via time-frequency masking. IEEE Trans. Signal Processing 52(7), 1830–1847 (2004)
Araki, S., Sawada, H., Mukai, R., Makino, S.: Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors. Signal Processing 77(8), 1833–1847 (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. Audio, Speech and Language Processing 15(5), 1592–1604 (2007)
Bishop, C.M.: Pattern recognition and machine learning. Springer, Heidelberg (2008)
Mandel, M., Ellis, D., Jebara, T.: An EM algorithm for localizing multiple sound sources in reverberant environments. In: Proc. Neural Info. Proc. Sys. (2006)
O’Grady, P., Pearlmutter, B.: Soft-LOST: EM on a mixture of oriented lines. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 430–436. Springer, Heidelberg (2004)
Smaragdis, P., Boufounos, P.: Learning source trajectories using wrapped-phase hidden markov models. In: Proc. of WASPAA 2005, pp. 114–117 (2005)
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
Araki, S., Nakatani, T., Sawada, H., Makino, S. (2009). Stereo Source Separation and Source Counting with MAP Estimation with Dirichlet Prior Considering Spatial Aliasing Problem. 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_93
Download citation
DOI: https://doi.org/10.1007/978-3-642-00599-2_93
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)