Speech Compression Using Multecirculerletet Transform

Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, suc...

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Autores principales:	Sulaiman Murtadha, Ali. K. Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2012
Materias:	Sound Speech Compression MCT DWT Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/e149b64626f14d52a13dc35e661035bd
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id	oai:doaj.org-article:e149b64626f14d52a13dc35e661035bd
record_format	dspace
spelling	oai:doaj.org-article:e149b64626f14d52a13dc35e661035bd2021-12-02T07:16:40ZSpeech Compression Using Multecirculerletet Transform1818-11712312-0789https://doaj.org/article/e149b64626f14d52a13dc35e661035bd2012-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/143https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of compression ratio (CR), mean square error (MSE) and peak signal to noise ratio (PSNR) are assessed. Computer simulation results indicate that the two dimensions MCT offer a better compression ratio, MSE and PSNR than DWT. Sulaiman MurtadhaAli. K. IbrahimAl-Khwarizmi College of Engineering – University of BaghdadarticleSoundSpeech CompressionMCTDWTChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 8, Iss 4 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Sound Speech Compression MCT DWT Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Sound Speech Compression MCT DWT Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Sulaiman Murtadha Ali. K. Ibrahim Speech Compression Using Multecirculerletet Transform
description	Compressing the speech reduces the data storage requirements, leading to reducing the time of transmitting the digitized speech over long-haul links like internet. To obtain best performance in speech compression, wavelet transforms require filters that combine a number of desirable properties, such as orthogonality and symmetry.The MCT bases functions are derived from GHM bases function using 2D linear convolution .The fast computation algorithm methods introduced here added desirable features to the current transform. We further assess the performance of the MCT in speech compression application. This paper discusses the effect of using DWT and MCT (one and two dimension) on speech compression. DWT and MCT performances in terms of compression ratio (CR), mean square error (MSE) and peak signal to noise ratio (PSNR) are assessed. Computer simulation results indicate that the two dimensions MCT offer a better compression ratio, MSE and PSNR than DWT.
format	article
author	Sulaiman Murtadha Ali. K. Ibrahim
author_facet	Sulaiman Murtadha Ali. K. Ibrahim
author_sort	Sulaiman Murtadha
title	Speech Compression Using Multecirculerletet Transform
title_short	Speech Compression Using Multecirculerletet Transform
title_full	Speech Compression Using Multecirculerletet Transform
title_fullStr	Speech Compression Using Multecirculerletet Transform
title_full_unstemmed	Speech Compression Using Multecirculerletet Transform
title_sort	speech compression using multecirculerletet transform
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2012
url	https://doaj.org/article/e149b64626f14d52a13dc35e661035bd
work_keys_str_mv	AT sulaimanmurtadha speechcompressionusingmultecirculerletettransform AT alikibrahim speechcompressionusingmultecirculerletettransform
_version_	1718399548932489216

Speech Compression Using Multecirculerletet Transform

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