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On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding

Javaid Ahmad Sheikh, Sakeena Akhtar, Sahir Majeed, Mehboob-ul-Amin, Shabir Ahmad Parah. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Javaid Ahmad Sheikh, Sakeena Akhtar, Sahir Majeed, Mehboob-ul-Amin, Shabir Ahmad Parah

Javaid Ahmad Sheikh, Sakeena Akhtar, Sahir Majeed, Mehboob-ul-Amin and Shabir Ahmad Parah. Article: On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding. Communications on Applied Electronics 4(9):36-40, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Javaid Ahmad Sheikh and Sakeena Akhtar and Sahir Majeed and Mehboob-ul-Amin and Shabir Ahmad Parah},
	title = {Article: On the Design and Performance Evaluation of DWT based Compressed Speech Transmission with Convolutional Coding},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {9},
	pages = {36-40},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


With the recent growth and advancement in multimedia based applications, many new techniques capable of producing good quality of compressed speech have been developed. Thus over the past few years there has been enough work done on compression and enhancement of speech signals. In this paper, the application of Discrete Wavelet Transform for speech compression along with the Convolutional Coding for error detection and correction has been described. A convolutionally encoded 8-DPSK modulated bit stream is transmitted through an AWGN channel. At the decoder the received binary bit stream is demodulated and decoded using Viterbi decoder. The compression is performed using Db10 Wavelets with Hard and Soft Thresholding algorithms. To evaluate the performance of the proposed technique, original and the reconstructed signal at the decoder are compared and various performance parameters in terms of Mean Square Error, Peak signal to Noise Ratio, Retained Signal Energy and Compression Ratio have been calculated. From the results obtained, it is observed that speech compression using Discrete Wavelet Transform along with Convolution Coding, shows better performance in terms of PSNR and MSE (high PSNR and low MSE) as compared to speech compression without convolution Coding. The proposed technique has a lot of scope in wireless communications where bandwidth and Quality of Service (QOS) are two main important factors that are taken into considerations.


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Speech Compression; Db10 Wavelet; Hard Thresholding; Soft Thresholding; Convolution Coding