{"title":"Adaptive Filtering in Subbands for Supervised Source Separation","authors":"Bruna Luisa Ramos Prado Vasques, Mariane Rembold Petraglia, Antonio Petraglia","volume":132,"journal":"International Journal of Computer and Information Engineering","pagesStart":1294,"pagesEnd":1299,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10008312","abstract":"This paper investigates MIMO (Multiple-Input
\r\nMultiple-Output) adaptive filtering techniques for the application
\r\nof supervised source separation in the context of convolutive
\r\nmixtures. From the observation that there is correlation among the
\r\nsignals of the different mixtures, an improvement in the NSAF
\r\n(Normalized Subband Adaptive Filter) algorithm is proposed in
\r\norder to accelerate its convergence rate. Simulation results with
\r\nmixtures of speech signals in reverberant environments show the
\r\nsuperior performance of the proposed algorithm with respect to the
\r\nperformances of the NLMS (Normalized Least-Mean-Square) and
\r\nconventional NSAF, considering both the convergence speed and
\r\nSIR (Signal-to-Interference Ratio) after convergence.","references":"[1] S. S. Haykin, Adaptive Filter Theory. Upper Saddle River, 4th Ed., N.J:\r\nPrentice, 2002.\r\n[2] A. H. Sayed, Adaptive Filters. Wiley, 2008.\r\n[3] B. Farhang-Boroujeny, Adaptive Filters: Theory and Applications.\r\nWiley, 1998.\r\n[4] P. Smaragdis, B. Raj, and M. Shashanka, \u201cBlind source separation:\r\nstatistical principles,\u201d Independent Component Analysis and Signal\r\nSeparation: 7th International Conference, pp.414\u2013421, Sep. 2007.\r\n[5] J. F. Cardoso, \u201cSupervised and Semi-supervised Separation of Sounds\r\nfrom Single-Channel Mixtures,\u201d in Proceedings of the IEEE, v. 9, no.\r\n10, pp. 2009\u20132025, Oct. 1998.\r\n[6] D. Ellis, Prediction-driven computational auditory scene analysis. Ph.D.\r\ndissertation, MIT, Jun. 1998.\r\n[7] M. Zibulevsky and B. Pearlmutter, \u201cBlind source separation by sparse\r\ndecomposition in a signal dictionary,\u201d Neural Computation, v. 13, no.\r\n4, pp. 863\u2013882, Apr. 2001.\r\n[8] K. A. Lee and W. S. Gan, \u201cImproving Convergence of the\r\nNLMS Algorithm Using Constrained Subband Updates,\u201d IEEE Signal\r\nProcessing Letters, v. 11, no. 9, pp. 736\u2013739, 2004.\r\n[9] J. J. Shynk, \u201cFrequency domain and multirate adaptive filtering,\u201d IEEE\r\nSignal Processing Mag., v. 9, pp. 14\u201337, Jan. 1992.\r\n[10] P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical\r\nImplementation. Springer US, 4th Ed., 2013.\r\n[11] K.A. Lee, W.S. Gan, S.M. Kuo Subband adaptive Filtering: Theory\r\nand Implementation. Wiley, Hoboken, NJ, 2009.\r\n[12] S. K. Mitra Digital Signal Processing: A Computer-Based Approach.\r\nMcGraw-Hill Higher Education, 2nd Ed. 2000.\r\n[13] Lehmann, E., Johansson, A., Nordholm, S., \u201c Reverberation-Time\r\nPrediction Method for Room Impulse Responses Simulated with the\r\nImage-Source Model,\u201d in IEEE Workshop on Applications of Signal\r\nProcessing to Audio and Acoustics, 2007, pp. 159\u2013162, Oct 2007.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 132, 2017"}