Abstract
We present a method of blind source separation (BSS) for speech signals using a complex extension of infinite sparse factor analysis (ISFA) in the frequency domain. Our method is robust against delayed signals that usually occur in real environments, such as reflections, short-time reverberations, and time lags of signals arriving at microphones. ISFA is a conventional non-parametric Bayesian method of BSS, which has only been applied to time domain signals because it can only deal with real signals. Our method uses complex normal distributions to estimate source signals and mixing matrix. Experimental results indicate that our method outperforms the conventional ISFA in the average signal-to-distortion ratio (SDR).
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
Wölfel, M., McDonough, J.: Distant Speech Recognition. Wiley (2009)
Seltzer, M.L., Raj, B., Stern, R.M.: Likelihood-maximizing beamforming for robust hands-free speech recognition. IEEE Trans. on Speech and Audio Processing 12(5), 489–498 (2004)
Nakadai, K., Takahashi, T., Okuno, H.G., Nakajima, H., Hasegawa, Y., Tsujino, H.: Design and Implementation of Robot Audition System ”HARK” Open Source Software for Listening to Three Simultaneous Speakers. Advanced Robotics 24(5–6), 739–761 (2010)
Valin, J.M., Rouat, J., Michaud, F.: Enhanced robot audition based on microphone array source separation with post-filter. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004, vol. 3, pp. 2123–2128. IEEE (2004)
Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Transactions on Signal Processing 45(2), 434–444 (1997)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent component analysis. Wiley Interscience (2001)
Knowles, D., Ghahramani, Z.: Infinite Sparse Factor Analysis and Infinite Independent Components Analysis. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds.) ICA 2007. LNCS, vol. 4666, pp. 381–388. Springer, Heidelberg (2007)
Griffiths, T., Ghahramani, Z.: Infinite latent feature models and the Indian buffet process. Advances in Neural Information Processing Systems 18, 475–482 (2006)
Meeds, E., Ghahramani, Z., Neal, R.M., Roweis, S.T.: Modeling dyadic data with binary latent factors. Advances in Neural Information Processing Systems 19, 977–984 (2007)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41(1-4), 1–24 (2001)
Sawada, H., Mukai, R., Araki, S., Makino, S.: A robust and precise method for solving the permutation problem of frequency-domain blind source separation. IEEE Trans. on Speech and Audio Processing 12(5), 530–538 (2004)
Vincent, E., Sawada, H., Bofill, P., Makino, S., Rosca, J.P.: 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
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nagira, K., Takahashi, T., Ogata, T., Okuno, H.G. (2012). Complex Extension of Infinite Sparse Factor Analysis for Blind Speech Separation. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-28551-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
Online ISBN: 978-3-642-28551-6
eBook Packages: Computer ScienceComputer Science (R0)