Abstract
For short-time Fourier Transform (STFT) domain ICA, dealing with reverberant sounds is a significant issue. It often invites a dilemma on STFT frame length: frames shorter than reverberation time (short frames) generate incomplete instantaneous mixtures, while too long frames may disturb the separation.
To improve the separation of such reverberant sounds, the authors propose a new framework which accounts for STFT with short frames. In this framework, time domain convolutive mixtures are transformed to STFT domain convolutive mixtures. For separating the mixtures, an approach of applying another STFT is presented so as to treat them as instantaneous mixtures.
The authors experimentally confirmed that this framework outperforms the conventional STFT domain ICA.
This is a preview of subscription content, log in via an 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
Smaragdis, P.: Blind separation of convolved mixtures in the frequency domain. Neurocomputating 10(2), 251–276 (1998)
Araki, S., Makino, S., Mukai, R., Nishikawa, T., Saruwatari, H.: Fundamental limitation of frequency domain blind source separation for convolved mixture of speech. In: Proc. ICA 2001, pp. 132–137 (December 2001)
Servière, C.: Separation of speech signals under reverberant conditions. In: Proc. EUSIPCO 2004, pp. 1693–1696 (2004)
Amari, S., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: Advances in Neural Information Processing Systems, vol. 8, MIT Press, Cambridge (1996)
Hiroe, A.: Solution of Permutation Problem in Frequency Domain ICA, Using Multivariate Probability Density Functions. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 601–608. Springer, Heidelberg (2006)
Matsuoka, K., Nakashima, S.: Minimal distortion principle for blind source separation. In: Proc. ICA 2001, pp.722–727 (December 2001)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis (2001)
Greenberg, S., Kingsbury, B.E.D.: The Modulation Spectrogram. In: Pursuit Of An Invariant Representation Of Speech. In: Proc. ICASSP 1997, pp. 1647–1650 (1997)
Rabiner, L., Schafer, R.: Short-Time Fourier Analysis. In: Digital Processing of Speech Signals, Prentice-Hall, London (1978)
Kim, T., Eltoft, T., Lee, T.-W.: Independent Vector Analysis: an extension of ICA to multivariate components. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 165–172. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hiroe, A. (2007). Blind Vector Deconvolution: Convolutive Mixture Models in Short-Time Fourier Transform Domain. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_59
Download citation
DOI: https://doi.org/10.1007/978-3-540-74494-8_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74493-1
Online ISBN: 978-3-540-74494-8
eBook Packages: Computer ScienceComputer Science (R0)