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: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Al-Khwarizmi College of Engineering – University of Baghdad
2012
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/e149b64626f14d52a13dc35e661035bd |
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| Sumario: | 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.
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