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Adaptive Filtering in Subbands for Supervised Source Separation

Authors: Bruna Luisa Ramos Prado Vasques, Mariane Rembold Petraglia, Antonio Petraglia


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.

Keywords: Adaptive filtering, multirate processing, normalized subband adaptive filter, source separation.

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[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.