Abstract
Conventional Independent Component Analysis (ICA) in frequency domain inherently causes the permutation problem. To solve the problem fundamentally, we propose a new framework for separation of the whole spectrograms instead of the conventional binwise separation. Under our framework, a measure of independence is calculated from the whole spectrograms, not individual frequency bins. For the calculation, we introduce some multivariate probability density functions (PDFs) which take a spectrum as arguments. To seek the unmixing matrix that makes spectrograms independent, we demonstrate a gradient-based algorithm using multivariate activation functions derived from the PDFs. Through experiments using real sound data, we have confirmed that our framework is effective to generate permutation-free unmixed results.
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
Smaragdis, P.: Blind separation of convolved mixtures in the frequency domain. Neurocomputating 10(2), 251–276 (1998)
Sawada, H., Mukai, R., Araki, S., Makino, S.: A Robust and Precise Method for Solving the Permutation Problem of Frequency-Domain Blind Source Separation. In: Proc. ICA 2003, pp. 505–510 (April 2003)
Mitianoudis, N., Davies, M.: Audio source separation of convolutive mixtures. Trans. Audio and Speech Processing 11(5), 489–497 (2003)
Davies, M.: Mathematics in Signal Processing V. In: Audio Source Separation. Oxford University Press, Oxford (2002)
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)
http://www.quantlet.com/mdstat/scripts/mva/htmlbook/mvahtmlnode42.html
Matsuoka, K., Nakashima, S.: Minimal distortion principle for blind source separation. In: Proc. ICA 2001, pp. 722–727 (December 2001)
Févotte, C., Gribonval, R., Vincent, E.: BSS EVAL Toolbox User Guide, IRISA Technical Report 1706, Rennes, France (April 2005), http://www.irisa.fr/metiss/bss_eval/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hiroe, A. (2006). Solution of Permutation Problem in Frequency Domain ICA, Using Multivariate Probability Density Functions. In: Rosca, J., Erdogmus, D., PrÃncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_75
Download citation
DOI: https://doi.org/10.1007/11679363_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)