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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2021-Jun-30
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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: 1,901 | Views: 6,206

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

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 3
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Cited-By CrossRef

[1] An Adaptive Sparse Algorithm for Synthesizing Note Specific Atoms by Spectrum Analysis, Applied to Music Signal Separation, AZAMIAN, M., KABIR, E., SEYEDIN, S., MASEHIAN, E., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.02014
[CrossRef] [Full text]

[2] Convolution separation and application of joint diagonalization with optimal parameters on mechanical signals, Zhang, Yuanyuan, Xin, Jianghui, Journal of Vibroengineering, ISSN 1392-8716, Issue 8, Volume 23, 2021.
Digital Object Identifier: 10.21595/jve.2021.21961
[CrossRef]

[3] Binary spectral masking for speech recognition systems, Versiani, Thiago de Souza Siqueira, Rodrigues, Gustavo Fernandes, Souza, Ana Claudia Silva de, Moreira, Jussara de Matos, Yehia, Hani Camille, 2012 35th International Conference on Telecommunications and Signal Processing (TSP), ISBN 978-1-4673-1118-2, 2012.
Digital Object Identifier: 10.1109/TSP.2012.6256330
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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