Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information

Hussein Mohammed Salman

doi:doi:10.11648/j.scidev.20210201.11

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Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information

Hussein Mohammed Salman

Published in Science Development (Volume 2, Issue 1)

Received: 30 November 2020 Accepted: 14 December 2020 Published: 4 January 2021

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Abstract

One of the still problems in the Digital Signals Processing is the Blind Signal (Source) Separation (BSS). The BSS mean how to recover the original (source) signals from mixed (observed) signals via many sensors. There are many methods are used in the Blind Signal (Source) Separation problems specifically Cocktail Party problem, such as Independent Component Analysis (ICA), which has become most commonly used. Also, In more cases of the BSS problems especially the Cocktail-Party case there are number of challenges as number of mixed signals and the mixture type. In order, to enhance the performance of the ICA there are many studies for this purpose that depend on the optimization mechanisms as genetic algorithm and Particle Swarm Optimization (PSO). The advantages of a Quantum Particle Swarm Optimization (QPSO) are employed to improve the efficiency of the ICA approach using mutual information function as modern technique, which is used in de-mixing of the speech signals. In this work, a new technique is introduced, is QPSO-based ICA by using Mutual Information function as an objective function for the optimizing process. The presented method has been implemented on the real three different speech signals, with 8 KHz frequency. The results was high accuracy in the signals and more efficient in the computations requirements as the time and space which are measured by the evaluation metrics as the signal plotting, SNR, SDR, and Computation Time.

Published in	Science Development (Volume 2, Issue 1)
DOI	10.11648/j.scidev.20210201.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

BSS, Mutual Information, ICA, QPSO, Cocktail-Party Problem

References

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[2]	A. Hyvarinen, Juha Karhunen, and Erkki Oja, 2001, “Independent Component Analysis”, john wily & son.
[3]	A. Tharwat, “Independent Component Analysis: An Introduction”, 2018, Applied Computing and Informatics, Elsevier, King Saud University.
[4]	H. M. Salman, 2019, “Mono Speech Signal Separation Using Optimize Independent Component Analysis Algorithm”, Ph. D. Dissertation, University of Babylon, Iraq.
[5]	A. Nidaa Abbas, M. Hussein Salman, 2020, “Enhancing Linear Independent Component Analysis: Comparison of Various Metaheuristic Methods”, Iraqi Journal for Electrical and Electronic Engineering, vol. 16, Issue 1, 113-122, https://doi.org/10.37917/ijeee.16.1.14.
[6]	S. B. Sadkhan, A. J. Alnaji and N. A. Muhsin, 2006, “performance evaluation algorithms based on neural networks”, CSNDSP (5th International Symposium on Communication Systems, Networks and Digital Signal Processing), Greece.
[7]	K. Zhang, G. Tian, and L. Tian, 2015,” Blind source separation based on JADE algorithm and application “, 3rd ICMRA-2015.
[8]	A. Hyvarinen, 2016, “Independent Component Analysis: recent advances”, royal society publishing.
[9]	R. Mutihac, and M. M. Van Hulle, 2009, “A comparative survey on adaptive neural network algorithms for independent component analysis “, Faculty of physics, university of Bucharest, 76900 Romania.
[10]	V. Krishnaveni, S. Jayaraman, P. M. Manoj Kumar, K. Shivakumar, K. Ramadoss, 2005, ”comparison of independent component analysis algorithms for removal of ocular artifacts from electroencephalogram”, Measurement Science Review, vol-5, sec-2, 67-78.
[11]	Sun J, Lai C., and Wu X., 2012, “particle swarm optimization classical and quantum perspectives”, CRC-press.
[12]	J. Sun, B. Feng, and W. Xu, 2004, “particle swarm optimization with particles having quantum behavior”, IEEE, DOI: 10.1109/CEC.2004.1330875.
[13]	B. Paprocki, A. Pregowska, and J. Szczepanski, 2020, “Optimizing information processing in brain-inspired neural networks”, bulletin of the polish academy of sciences technical sciences, Vol. 68, No. 2.
[14]	D. Fantinato, L. Duarte, Y. Deville, R. Attux, C. Jutten, and A. Neves, 2019, “A second-order statistics method for blind source separation in post-nonlinear mixtures”, Signal Processing, Elsevier, 155 (2019) 63-72.
[15]	A. Hyvarinen, 1999, "Survey on Independent Component Analysis", Neural Computing Surveys 2, 94-128, 1999.
[16]	J. Kennedy and R. Eberhart, 1995, “particle swarm optimization “, proceeding of the IEEE international conference on neural networks, Australia.
[17]	M. Xi, J. Sun, and W. Xu, 2008, “An improved quantum-behaved particle swarm optimization with weighted mean best position”, journal of applied mathematics and computation, Elsevier, vol. 205 (2008), pp 751–759, DOI: 10.1016/j.amc.2008.05.135.
[18]	N. A. Abbas, 2015, “Image encryption based on Independent Component Analysis and Arnold’s Cat Map”, Egyptian Informatics Journal, dx.doi.org/10.1016/j.eij.2015.10.001.
[19]	E. Vincent, R. Gribonval, C. Fevotte, 2006,” Performance measurement in blind audio source separation”, IEEE, vol. 14, issue 4, pp: 1462-1469 DOI: 10.1109/TSA.2005.858005.
[20]	https://github.com/dennisguse/ITU-T_pesq/tree/master/comform, accessed at 2019.
[21]	http://www.utdallas.edu/~loizou/speech/noizeus/, accessed at 2019.

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Hussein Mohammed Salman. (2021). Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information. Science Development, 2(1), 1-6. https://doi.org/10.11648/j.scidev.20210201.11

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Hussein Mohammed Salman. Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information. Sci. Dev. 2021, 2(1), 1-6. doi: 10.11648/j.scidev.20210201.11

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AMA Style

Hussein Mohammed Salman. Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information. Sci Dev. 2021;2(1):1-6. doi: 10.11648/j.scidev.20210201.11

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@article{10.11648/j.scidev.20210201.11,
  author = {Hussein Mohammed Salman},
  title = {Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information},
  journal = {Science Development},
  volume = {2},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.scidev.20210201.11},
  url = {https://doi.org/10.11648/j.scidev.20210201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.scidev.20210201.11},
  abstract = {One of the still problems in the Digital Signals Processing is the Blind Signal (Source) Separation (BSS). The BSS mean how to recover the original (source) signals from mixed (observed) signals via many sensors. There are many methods are used in the Blind Signal (Source) Separation problems specifically Cocktail Party problem, such as Independent Component Analysis (ICA), which has become most commonly used. Also, In more cases of the BSS problems especially the Cocktail-Party case there are number of challenges as number of mixed signals and the mixture type. In order, to enhance the performance of the ICA there are many studies for this purpose that depend on the optimization mechanisms as genetic algorithm and Particle Swarm Optimization (PSO). The advantages of a Quantum Particle Swarm Optimization (QPSO) are employed to improve the efficiency of the ICA approach using mutual information function as modern technique, which is used in de-mixing of the speech signals. In this work, a new technique is introduced, is QPSO-based ICA by using Mutual Information function as an objective function for the optimizing process. The presented method has been implemented on the real three different speech signals, with 8 KHz frequency. The results was high accuracy in the signals and more efficient in the computations requirements as the time and space which are measured by the evaluation metrics as the signal plotting, SNR, SDR, and Computation Time.},
 year = {2021}
}

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TY  - JOUR
T1  - Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information
AU  - Hussein Mohammed Salman
Y1  - 2021/01/04
PY  - 2021
N1  - https://doi.org/10.11648/j.scidev.20210201.11
DO  - 10.11648/j.scidev.20210201.11
T2  - Science Development
JF  - Science Development
JO  - Science Development
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2994-7154
UR  - https://doi.org/10.11648/j.scidev.20210201.11
AB  - One of the still problems in the Digital Signals Processing is the Blind Signal (Source) Separation (BSS). The BSS mean how to recover the original (source) signals from mixed (observed) signals via many sensors. There are many methods are used in the Blind Signal (Source) Separation problems specifically Cocktail Party problem, such as Independent Component Analysis (ICA), which has become most commonly used. Also, In more cases of the BSS problems especially the Cocktail-Party case there are number of challenges as number of mixed signals and the mixture type. In order, to enhance the performance of the ICA there are many studies for this purpose that depend on the optimization mechanisms as genetic algorithm and Particle Swarm Optimization (PSO). The advantages of a Quantum Particle Swarm Optimization (QPSO) are employed to improve the efficiency of the ICA approach using mutual information function as modern technique, which is used in de-mixing of the speech signals. In this work, a new technique is introduced, is QPSO-based ICA by using Mutual Information function as an objective function for the optimizing process. The presented method has been implemented on the real three different speech signals, with 8 KHz frequency. The results was high accuracy in the signals and more efficient in the computations requirements as the time and space which are measured by the evaluation metrics as the signal plotting, SNR, SDR, and Computation Time.
VL  - 2
IS  - 1
ER  -

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Author Information

Hussein Mohammed Salman

College of Material Engineering, University of Babylon, Babylon, Iraq

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Hussein Mohammed Salman. (2021). Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information. Science Development, 2(1), 1-6. https://doi.org/10.11648/j.scidev.20210201.11

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Hussein Mohammed Salman. Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information. Sci. Dev. 2021, 2(1), 1-6. doi: 10.11648/j.scidev.20210201.11

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AMA Style

Hussein Mohammed Salman. Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information. Sci Dev. 2021;2(1):1-6. doi: 10.11648/j.scidev.20210201.11

Copy | Download

@article{10.11648/j.scidev.20210201.11,
  author = {Hussein Mohammed Salman},
  title = {Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information},
  journal = {Science Development},
  volume = {2},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.scidev.20210201.11},
  url = {https://doi.org/10.11648/j.scidev.20210201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.scidev.20210201.11},
  abstract = {One of the still problems in the Digital Signals Processing is the Blind Signal (Source) Separation (BSS). The BSS mean how to recover the original (source) signals from mixed (observed) signals via many sensors. There are many methods are used in the Blind Signal (Source) Separation problems specifically Cocktail Party problem, such as Independent Component Analysis (ICA), which has become most commonly used. Also, In more cases of the BSS problems especially the Cocktail-Party case there are number of challenges as number of mixed signals and the mixture type. In order, to enhance the performance of the ICA there are many studies for this purpose that depend on the optimization mechanisms as genetic algorithm and Particle Swarm Optimization (PSO). The advantages of a Quantum Particle Swarm Optimization (QPSO) are employed to improve the efficiency of the ICA approach using mutual information function as modern technique, which is used in de-mixing of the speech signals. In this work, a new technique is introduced, is QPSO-based ICA by using Mutual Information function as an objective function for the optimizing process. The presented method has been implemented on the real three different speech signals, with 8 KHz frequency. The results was high accuracy in the signals and more efficient in the computations requirements as the time and space which are measured by the evaluation metrics as the signal plotting, SNR, SDR, and Computation Time.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Speech Signals Separation Using Optimized Independent Component Analysis and Mutual Information
AU  - Hussein Mohammed Salman
Y1  - 2021/01/04
PY  - 2021
N1  - https://doi.org/10.11648/j.scidev.20210201.11
DO  - 10.11648/j.scidev.20210201.11
T2  - Science Development
JF  - Science Development
JO  - Science Development
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2994-7154
UR  - https://doi.org/10.11648/j.scidev.20210201.11
AB  - One of the still problems in the Digital Signals Processing is the Blind Signal (Source) Separation (BSS). The BSS mean how to recover the original (source) signals from mixed (observed) signals via many sensors. There are many methods are used in the Blind Signal (Source) Separation problems specifically Cocktail Party problem, such as Independent Component Analysis (ICA), which has become most commonly used. Also, In more cases of the BSS problems especially the Cocktail-Party case there are number of challenges as number of mixed signals and the mixture type. In order, to enhance the performance of the ICA there are many studies for this purpose that depend on the optimization mechanisms as genetic algorithm and Particle Swarm Optimization (PSO). The advantages of a Quantum Particle Swarm Optimization (QPSO) are employed to improve the efficiency of the ICA approach using mutual information function as modern technique, which is used in de-mixing of the speech signals. In this work, a new technique is introduced, is QPSO-based ICA by using Mutual Information function as an objective function for the optimizing process. The presented method has been implemented on the real three different speech signals, with 8 KHz frequency. The results was high accuracy in the signals and more efficient in the computations requirements as the time and space which are measured by the evaluation metrics as the signal plotting, SNR, SDR, and Computation Time.
VL  - 2
IS  - 1
ER  -

Copy | Download