Sub–Nyquist Frequency Efficient Audio Compression

This paper presents the application of a framework of fast and efficient compressive sampling based on the concept of random sampling of sparse Audio signal. It provides four important features. (i) It is universal with a variety of sparse signals. (ii) The number of measurements required for exact...

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Autor principal:	Ahmed A. Hashim
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2012
Materias:	Sub-Nyquist Sampling Compressive Sampling Compressed Sensing Nonlinear Reconstruction Random Matrices Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/a1933c09ea5846fb8df421d4698a0c4f
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id	oai:doaj.org-article:a1933c09ea5846fb8df421d4698a0c4f
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spelling	oai:doaj.org-article:a1933c09ea5846fb8df421d4698a0c4f2021-12-02T06:35:46ZSub–Nyquist Frequency Efficient Audio Compression1818-11712312-0789https://doaj.org/article/a1933c09ea5846fb8df421d4698a0c4f2012-08-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/138https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 This paper presents the application of a framework of fast and efficient compressive sampling based on the concept of random sampling of sparse Audio signal. It provides four important features. (i) It is universal with a variety of sparse signals. (ii) The number of measurements required for exact reconstruction is nearly optimal and much less then the sampling frequency and below the Nyquist frequency. (iii) It has very low complexity and fast computation. (iv) It is developed on the provable mathematical model from which we are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction of the audio signal. Compressed sensing CS is an attractive compression scheme due to its universality and lack of complexity on the sensor side. In this paper a study of applying compressed sensing on audio signals was presented. The performance of different bases and its reconstruction are investigated, as well as exploring its performance. Simulations results are present to show the efficient reconstruction of sparse audio signal. The results shows that compressed sensing can dramatically reduce the number of samples below the Nyquist rate keeping with a good PSNR. Ahmed A. HashimAl-Khwarizmi College of Engineering – University of BaghdadarticleSub-Nyquist SamplingCompressive SamplingCompressed SensingNonlinear ReconstructionRandom MatricesChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 8, Iss 3 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sub-Nyquist Sampling Compressive Sampling Compressed Sensing Nonlinear Reconstruction Random Matrices Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Sub-Nyquist Sampling Compressive Sampling Compressed Sensing Nonlinear Reconstruction Random Matrices Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Ahmed A. Hashim Sub–Nyquist Frequency Efficient Audio Compression
description	This paper presents the application of a framework of fast and efficient compressive sampling based on the concept of random sampling of sparse Audio signal. It provides four important features. (i) It is universal with a variety of sparse signals. (ii) The number of measurements required for exact reconstruction is nearly optimal and much less then the sampling frequency and below the Nyquist frequency. (iii) It has very low complexity and fast computation. (iv) It is developed on the provable mathematical model from which we are able to quantify trade-offs among streaming capability, computation/memory requirement and quality of reconstruction of the audio signal. Compressed sensing CS is an attractive compression scheme due to its universality and lack of complexity on the sensor side. In this paper a study of applying compressed sensing on audio signals was presented. The performance of different bases and its reconstruction are investigated, as well as exploring its performance. Simulations results are present to show the efficient reconstruction of sparse audio signal. The results shows that compressed sensing can dramatically reduce the number of samples below the Nyquist rate keeping with a good PSNR.
format	article
author	Ahmed A. Hashim
author_facet	Ahmed A. Hashim
author_sort	Ahmed A. Hashim
title	Sub–Nyquist Frequency Efficient Audio Compression
title_short	Sub–Nyquist Frequency Efficient Audio Compression
title_full	Sub–Nyquist Frequency Efficient Audio Compression
title_fullStr	Sub–Nyquist Frequency Efficient Audio Compression
title_full_unstemmed	Sub–Nyquist Frequency Efficient Audio Compression
title_sort	sub–nyquist frequency efficient audio compression
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2012
url	https://doaj.org/article/a1933c09ea5846fb8df421d4698a0c4f
work_keys_str_mv	AT ahmedahashim subnyquistfrequencyefficientaudiocompression
_version_	1718399830113386496

Sub–Nyquist Frequency Efficient Audio Compression

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