Adaptive Filtering in Subbands for Supervised Source Separation

This paper investigates MIMO (Multiple-Input
Multiple-Output) adaptive filtering techniques for the application
of supervised source separation in the context of convolutive
mixtures. From the observation that there is correlation among the
signals of the different mixtures, an improvement in the NSAF
(Normalized Subband Adaptive Filter) algorithm is proposed in
order to accelerate its convergence rate. Simulation results with
mixtures of speech signals in reverberant environments show the
superior performance of the proposed algorithm with respect to the
performances of the NLMS (Normalized Least-Mean-Square) and
conventional NSAF, considering both the convergence speed and
SIR (Signal-to-Interference Ratio) after convergence.

Authors:

• Bruna Luisa Ramos Prado Vasques
• Mariane Rembold Petraglia
• Antonio Petraglia

Keywords:

• Adaptive filtering
• multirate processing
• normalized subband adaptive filter
• source separation.

References:

[1] S. S. Haykin, Adaptive Filter Theory. Upper Saddle River, 4th Ed., N.J:
Prentice, 2002.
[2] A. H. Sayed, Adaptive Filters. Wiley, 2008.
[3] B. Farhang-Boroujeny, Adaptive Filters: Theory and Applications.
Wiley, 1998.
[4] P. Smaragdis, B. Raj, and M. Shashanka, “Blind source separation:
statistical principles,” Independent Component Analysis and Signal
Separation: 7th International Conference, pp.414–421, Sep. 2007.
[5] J. F. Cardoso, “Supervised and Semi-supervised Separation of Sounds
from Single-Channel Mixtures,” in Proceedings of the IEEE, v. 9, no.
10, pp. 2009–2025, Oct. 1998.
[6] D. Ellis, Prediction-driven computational auditory scene analysis. Ph.D.
dissertation, MIT, Jun. 1998.
[7] M. Zibulevsky and B. Pearlmutter, “Blind source separation by sparse
decomposition in a signal dictionary,” Neural Computation, v. 13, no.
4, pp. 863–882, Apr. 2001.
[8] K. A. Lee and W. S. Gan, “Improving Convergence of the
NLMS Algorithm Using Constrained Subband Updates,” IEEE Signal
Processing Letters, v. 11, no. 9, pp. 736–739, 2004.
[9] J. J. Shynk, “Frequency domain and multirate adaptive filtering,” IEEE
Signal Processing Mag., v. 9, pp. 14–37, Jan. 1992.
[10] P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical
Implementation. Springer US, 4th Ed., 2013.
[11] K.A. Lee, W.S. Gan, S.M. Kuo Subband adaptive Filtering: Theory
and Implementation. Wiley, Hoboken, NJ, 2009.
[12] S. K. Mitra Digital Signal Processing: A Computer-Based Approach.
McGraw-Hill Higher Education, 2nd Ed. 2000.
[13] Lehmann, E., Johansson, A., Nordholm, S., “ Reverberation-Time
Prediction Method for Room Impulse Responses Simulated with the
Image-Source Model,” in IEEE Workshop on Applications of Signal
Processing to Audio and Acoustics, 2007, pp. 159–162, Oct 2007.