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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2011 - 10

Blind Source Separation for Convolutive Mixtures with Neural Networks

KIREI, B. S. See more information about KIREI, B. S. on SCOPUS See more information about KIREI, B. S. on IEEExplore See more information about KIREI, B. S. on Web of Science, TOPA, M. D. See more information about  TOPA, M. D. on SCOPUS See more information about  TOPA, M. D. on SCOPUS See more information about TOPA, M. D. on Web of Science, MURESAN, I. See more information about  MURESAN, I. on SCOPUS See more information about  MURESAN, I. on SCOPUS See more information about MURESAN, I. on Web of Science, HOMANA, I. See more information about  HOMANA, I. on SCOPUS See more information about  HOMANA, I. on SCOPUS See more information about HOMANA, I. on Web of Science, TOMA, N. See more information about TOMA, N. on SCOPUS See more information about TOMA, N. on SCOPUS See more information about TOMA, N. on Web of Science
 
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Download PDF pdficon (826 KB) | Citation | Downloads: 1,791 | Views: 6,052

Author keywords
blind source separation, neural networks, independent component analysis, subband analysis and synthesis

References keywords
separation(17), blind(14), source(12), topa(10), processing(10), marina(10), audio(10), signal(9), telecommunications(7), speech(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-02-27
Volume 11, Issue 1, Year 2011, On page(s): 63 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.01010
Web of Science Accession Number: 000288761800010
SCOPUS ID: 79955960740

Abstract
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Blind source separation of convolutive mixtures is used as a preprocessing stage in many applications. The aim is to extract individual signals from their mixtures. In enclosed spaces, due to reverberation, audio signal mixtures are considered to be convolutive ones. Time domain algorithms (as neural network based blind source separation) are not suitable for signal recovery from convolutive mixtures, thus the need of frequency domain or subband processing arise. We propose a subband approach: first the mixtures are split to several subbands, next time-domain blind source separation is carried out in each subband, finally the recovered sources are recomposed from the subbands. The major drawback of the subband approach is the unknown order of the recovered sources. Regardless of this undesired phenomenon the subband approach is faster and more stable than the simple time domain algorithm.


References | Cited By  «-- Click to see who has cited this paper

[1] P. Comon, C. Jutten, "Handbook of Blind Source Separation - Independent Component Analysis and Applications", Academic Press, 2010 The Boulevard, Langford Lane, Kidlington, Oxford, OX5 1GB, UK, ISBN: 978-0-12-374726-6

[2] Shoji Makino, Hiroshi Sawada and Te-Won Lee, "Blind Speech Separation", Springer, Dordrecht, The Nederlands, 2007, ISBN 978-1-4020-6478-4

[3] Shoji Makino, Shoko Araki, Ryo Mukai and Hiroshi Sawada, "Audio Source Separation Based on Independent Component Analysis", Invited in Proceedings of ISCAS 2004 (International Symposium on Circuits and Systems), vol. V, pp. 668-671, May 2004

[4] A. Aissa-El-Bey,K. Abed-Meraim, Y. Grenier, "Blind Audio Source Separation Using Sparsity Based Criterion for Convolutive Mixture Case", Lecture Notes In Computer Science - Proceedings of the 7th international conference on Independent component analysis and signal separation, London, UK, 9 - 12 September 2007, Pages: 317-324, ISBN 3-540-74493-2

[5] Hiroki Asari, Barak A. Pearlmutter, Anthony M. Zador, "Sparse Representations for the Cocktail Party Problem", The Journal of Neuroscience, July 12, 2006, pg. 7477-7490.
[CrossRef] [PubMed] [Web of Science Times Cited 33] [SCOPUS Times Cited 33]


[6] Dorothea Kolossa, Ramon Fernandez Astudillo, Eugen Hoffmann, and Reinhold Orglmeister, "Independent Component Analysis and Time-Frequency Masking for Speech Recognition in Multitalker Conditions", EURASIP Journal on Audio, Speech, and Music Processing, Volume 2010

[7] Harald Viste, Gianpaolo Evangelista, "Sound Source Separation: Processing for Hearing Aids and Structured Audio Coding", Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-01), Limerick, Ireland, December 6-8, 2001

[8] C. Jutten, J. Heraul, "Blind Separation of Sources, Part 1: An Adaptive Algorithm Based on Neurometric Architecture", Signal Processing, Volume 24, pp 1-10, 1991.
[CrossRef] [Web of Science Times Cited 1542] [SCOPUS Times Cited 1868]


[9] Hiroshi Sawada, Ryo Mukai, Shoko Araki, Shoji Makino, "Convolutive Blind Source Separation for More Than Two Sources in the Frequency Domain", Proceedings of The 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, 2004, Quebec, Canada

[10] Norbert TOMA, Marina Dana TOPA, Erwin SZOPOS, "Reverberation Algorithms", Acta Technica Napocensis - Electronics and Telecommunications, Volume 46, Number 2/2005, pp.27-34.

[11] Norbert TOMA, Marina Dana TOPA, Erwin SZOPOS , "Design and Performance Analysis of Reverberation Algorithms", Acta Technica Napocensis - Electronics and Telecommunications, Volume 48, Number 1/2007, pp. 35-43

[12] Irina DORNEAN, Marina TOPA, Botond Sandor KIREI - "Digital Implementation of Artificial Reverberation Algorithms", Acta Technica Napocensis - Electronics and Telecommunications, Volume 49, Number 4/2008, pp. 1-4

[13] Irina DORNEAN, Marina TOPA, Botond Sandor KIREI, Erwin SZOPOS, "FPGA Implementation of the Adaptive Least Mean Square Algorithm", Acta Technica Napocensis - Electronics and Telecommunications, Volume 48, Number 4/2007, pp.19-22

[14] Irina Dornean, Marina Topa, Botond Sandor Kirei, Marius Neag, "Sub-Band Adaptive Filtering for Acoustic Echo Cancellation", Proceedings of the European Conference on Circuit Theory and Design ECCTD 2009, ISBN 978-1-4244-3896-9, 23-27 August, 2009, Antalya, Turkey, pp. 810, 813 (on CD C3L-D-3)

[15] Marina Dana Topa, Irina Muresan, Botond Sandor Kirei, Ioana Homana, "Digital Adaptive Echo-Canceller for Room Acoustics Improvement", Advances in Electronical and Computer Engineering, Number 1, 2010, pp. 50-53
[CrossRef] [Full Text] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[16] Ioana Homana, Marina Topa, Botond Sandor Kirei, Cristian Contan, "Adaptive Algorithm for Double-Talk Echo Cancelling", Proceedings of the 2010 9th International Symposium on Electronics and Telecommunications, November 11-12, 2010, Timisoara, Romania

[17] Norbert Toma, Marina Topa, Irina Muresan, Botond Sandor Kirei, Marius Neag, Albert Fazakas, "Acoustic Modelling and Optimization of a Room", Acta Tehnica Napocensis - Electronics and Telecommunications, ISSN 1221-6542, Vol. 50, Nr. 2, 2009

[18] Paulo B. Batalheiro, Mariane R. Petraglia and Diego B. Haddad, "Online Subband Blind Source Separation for Convolutive Mixtures Using a Uniform Filter Bank with Critical Sampling", Independent Component Analysis and Signal Separation - Lecture Notes in Computer Science, 2009, Volume 5441/2009, pg. 211-218

[19] Shun-Ichi Amari, Andrzej Cichocki, "Adaptive Blind Signal Processing - Neural Network Approaches", Proceedings of IEEE, Vol. 86, No. 10, October 1998.

[20] Botond Sandor Kirei, Albert Fazakas, Marina Dana Topa, "Matlab Modeling and FPGA Implementation of Neuronal Algorithms for Blind Audio Signal Separation", Acta Technica Napocensis - Electronics and Telecommunications, Volume 47, Number 4/2006

[21] Simon Haykin: "Neural Networks: A Comprehensive Foundation - Second Edition", Prentice Hall, Upper Saddle River, NJ, 1999,

[22] Marina Topa, Sergiu Muresan, Florin Bud, "On Neural Algorithms for Blind Audio Source Separation", Advances in Numerical Computation Methods in Electromagnetism, Bruxelles, 2005, pp. 74-81, May 2005

[23] P. P. Vaidyanathan, "Multirate Systems and Filter Banks", Prentice Hall PTR, Upper Saddle River, New Jersey, 1993, ISBN 0-13-605718-7

[24] Bo Peng, Wei Liu and P. Mandic, "An Improved Solution to the Subband Blind Source Separation Permutation Problem Based on Optimized Filter Banks", Proceedings of Control and Signal Processing, ISCCSP 2010, Limassol, Cyprus, 2-5 March 2010

[25] E. Vincent, R. Gribonval, C. Fevotte, "Performance Measurement in Blind Audio Source Separation", IEEE Transactions on Audio, Speech, and Language Processing, Volume: 14, Issue 4, July 2006

[26] J.-F. Synnevag and T. Dahl, "Blind Source Separation for Convolutive Mixtures Using Spatially Resampled Observations", Proceedings of the 14th European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, September 4-8, 2006

[27] NQ Duong, E Vincent, and R Gribonval, "Under-determined Convolutive Blind Source Separation Using Spatial Covariance Models", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 15 - 19 2010, Dallas, USA

[28] Lin Wang, Heping Ding, and Fuliang Yin, "Combining Superdirective Beamforming and Frequency-Domain Blind Source Separation for Highly Reverberant Signals", EURASIP Journal on Audio, Speech, and Music Processing, Volume 2010



References Weight

Web of Science® Citations for all references: 1,579 TCR
SCOPUS® Citations for all references: 1,905 TCR

Web of Science® Average Citations per reference: 54 ACR
SCOPUS® Average Citations per reference: 66 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-11-26 01:36 in 22 seconds.




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