Improvement of convergence speed in FDICA algorithm with weighted inner product constraint of unmixing matrix

- Journal title : Phonetics and Speech Sciences
- Volume 7, Issue 4, 2015, pp.17-25
- Publisher : The Korean Society of Speech Sciences
- DOI : 10.13064/KSSS.2015.7.4.017

Title & Authors

Improvement of convergence speed in FDICA algorithm with weighted inner product constraint of unmixing matrix

Quan, Xingri; Bae, Keunsung;

Quan, Xingri; Bae, Keunsung;

Abstract

For blind source separation of convolutive mixtures, FDICA(Frequency Domain Independent Component Analysis) algorithms are generally used. Since FDICA algorithm such as Sawada FDICA, IVA(Independent Vector Analysis) works on the frequency bin basis with a natural gradient descent method, it takes much time to converge. In this paper, we propose a new method to improve convergence speed in FDICA algorithm. The proposed method reduces the number of iteration drastically in the process of natural gradient descent method by applying a weighted inner product constraint of unmixing matrix. Experimental results have shown that the proposed method achieved large improvement of convergence speed without degrading the separation performance of the baseline algorithms.

Keywords

Hadamard product form;FDICA;

Language

Korean

References

1.

P. Comon, (1994). Independent component analysis, a new concept?, Signal Processing, vol. 36, pp. 287-314.

2.

A. Hyvarinen, J. Karhunen, and E. Oja, (2001). Independent component analysis. John Wiley & Sons.

3.

A. Cichocki and S. Amari, (2003). Adaptive Blind Signal and Image Processing (Learning Algorithms and Applications). New York: John Wiley.

4.

A. J. Bell and T. J. Sejnowski, (1995). An information -maximization approach to blind separation and blind deconvolution. Neural Computation, vol. 7, pp. 1129-1159.

5.

S.-i. Amari, A. Cichocki, and H. H. Yang, (1996). A new learning algorithm for blind signal separation. Advances in neural information processing systems, pp. 757-763.

6.

A. Hyvarinen and E. Oja, (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, vol. 9, pp. 1483-1492.

7.

H. Sawada, R. Mukai, S. Araki, and S. Makino, (2001). A polar-coordinate based activation function for frequency domain blind source separation. in Proc. Int. Conf. ICA and BSS, pp. 663-668.

8.

Kim, T., H. T. Attias, Lee, S..-Y., and Lee, T.-W., (2007). Blind source separation exploiting higher-order frequency dependencies. Audio, Speech, and Language Processing, IEEE Transactions on, vol. 15, pp. 70-79.

9.

F. Nesta, P. Svaizer, and M. Omologo, (2011). Convolutive BSS of short mixtures by ICA recursively regularized across rrequencies. Audio, Speech, and Language Processing, IEEE Transactions on, vol. 19, pp. 624-639.

10.

K. Matsuoka, (2002). Minimal distortion principle for blind source separation. Proceedings of the 41st SICE Annual Conference, vol.4, pp. 2138-2143.

11.

H. Sawada, S. Araki, R. Mukai, and S. Makino, (2007). Grouping separated frequency components by estimating propagation model parameters in frequency-domain blind source separation. Audio, Speech, and Language Processing, IEEE Transactions on, vol. 15, pp. 1592-1604.

12.

H. Sawada, S. Araki, and S. Makino, (2007) Measuring dependence of bin-wise separated signals for permutation alignment in frequency-domain BSS. in Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on, pp. 3247-3250.

13.

Lee, I. and Jamg, G. J., (2012). Independent vector analysis based on overlapped cliques of variable width for frequency-domain blind signal separation. EURASIP Journal on Advances in Signal Processing, vol. 2012, p. 113.

14.

Lee, I., Kim, T. and Lee, T.-W., (2007). Fast fixed-point independent vector analysis algorithms for convolutive blind source separation. Signal Processing, vol. 87, pp. 1859-1871.

15.

S. Haykin, (2009). Neural networks and learning machines vol. 3. Prentice Hall.

16.

E. A. Lehmann. (2012). Image-source method: Matlab code implementation. www.eric-lehmann.com

17.

K. d. Donohue. (2009). Audio Systems Lab Experimental Data. http://www.engr.uky.edu/-donohue/audio/Data/audioexpdata.htm