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2 TIMIT NoisyNA Shi NoisyNA

3 Shi (NoisyNA) shi A ICA

4 PI SNIR

5 [1]. S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, Second Edition, John Wiley & Sons Ltd, [2]. M. Moonen, and A. Oostelink, editors, signal processing VI: Theoriesand applications, pp , Elsevier, [3]. oorelation in scalar signal in International conference on Acoustucs, Speech and Singal Processing, volume III, pp , [4]. sources: A Comparative study of a 2-nd and a 4- Signal Processing VII., Proceeding of EUSIPCO-94, pp , Lausanne, CH., EUSIP Association, [5]. In Advanced in neural information processing systems cambridg 1996, pp , MIT Press, [6]. IEEE Trans. Acoust. Speech Signal Process, Vol. ASSP-25, pp , June [7]. International Journal of Information Technology, vol.3, pp. 1-12, [8]. B. De Moor (Ed.), Daisy: database for the identification of systems ( [9]. Cichocki, S.I. Amari, Adaptive Blind Signal and Image Processing, Wiley, New York, [11]. Z. Shi, Z. Jiang, Z. Zhou, -point algorithm for blind source separation with Journal of Computational and Applied Mathematics, 223, pp , [12]. A. Hyvärinen, Blind source separation by nonstationarity of variance: A cumulantbased approach, IEEE Transactions on Neural Networks, 12 (6) pp , [13]. Z. Shi, C. Zhang, Pattern Recognition 42, pp , [15]. Mozaffari, M Packet Domains Using Laplacian Mixture Model Expectation Maximization Estimation in Over- Journal of Statistical Mechanics: Theory and Experiments An IOP and SISSA Journal, 2007, Issue 2, pp

6 [16]. Z. Shi, X. Tan, Z. Jiang, H. Zhang, C. Guo, nonlinear autocorrelation, 3rd International Congress on Image and Signal, pp , [17]. is Tool: Independent Component Regensburg, march 18 th [18]. -Maximization Approach to Blind Neural Computation,7, pp , [19]. a Geometry-based procedure for reconstruction of n- Signal Processing, 46, pp [20]. t fixed-point algorithms for independent component IEEE Transaction on neural networks, 10(3), pp , [21]. Neural computing Surveys, (2), pp , [22]. Hyvarinen, J. Karhunen, E. Oja. Independent Component Analysis, John Wiely & Sons Inc., [23]. -point algorithm for independent component neural computation,9, pp , [24]. ed approach to linear ICA Proceeding, San Diego. [25]. Extended infomax Algorithm for Mixed sub-gaussian and super- Neural Computation, 11, pp , [26]. Neurocomputing, 18, pp , [27]. ICA Proceeding, San Diego, [28]. Signal processing Elsevier, Vol. 36, pp , Special issue on Higher-Order Statistics, Apr [29]. Hyv IEEE Signal Processing Letter, vol. 6, pp , June [30]. A. Hyv rinen, Fast ICA for noisy data using gaussian moments. In Proc. Int. Symp. on Circuits and Systems, Orlando, Florida, 1999, pp [31]. IEEE Trans. Signal Processing, Vol. 41, pp , Dec [32]. S. Burrus, R. A. Gopinath, H. Guo, Prentice Hall Inc., upper Saddle River, New Jersy, [33]. IEEE Trans. Acoust. Speech Signal Process, Vol. ASSP-25, pp , June speech Analysis-s IEEE Tran. Audio Electroacoust., Vol. AU-21, No. 3, pp , June [34]. Proc. of the First ASSP Workshop on Spectral Estimation, pp , Aug [35]. 13th International Conference on Digital Signal Processing, Santorin-Greece, 1997.

7 [36]. IEEE Trans. on Signal Processing, Nov. 4, [37]., pp , [38]. Cancellation in the Underdetermined Case: a New Approach Based on Time- Proceeding of the 3rd International Conference on pp , San Diego, California, Dec. 9-13, [39]. L.T. Nguyen, A. Beloucharni, K. Abedsources than sensors using time-frequenc Int. Sym. on Signal Processing and its application (ISSPA), Kuala Lumpur, Malaysia, pp , Aug [40]. -timewww.cs.rochester.edu/u/rosca/preprints/2003/ica2003icalab1.pdf. [41]. Neurocomputing, Vol. 71, pp , [42]. nonlinear autocorrelation and non- Journal of Computational and Applied Mathematics, Vol. 229, Issue 1, pp. 240_247, [43]. Pattern Recognition Letter, Vol. 26, No. 16, pp , [44]., CBMS-NSF Regional conference series in Applied Mathemathics, Vol. 61, Published by: Society for Industrieal and Applied Mathematics, [45]. Stephane Mallat, A Wavelet tour of Signal Processing, Academic Press [46]. A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. on Pattern Analysis and machine Intelligence, Vol. 11, pp , 1989.

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