Comparing audio compression using wavelets with other audio compression schemes

Speech compression is the technology of converting human speech into an efficient encoded representation that can be decoded to produce a close approximation of the original signal. In this paper, we propose a new algorithm which compresses speech signals using a wavelet compression technique. The performance of this method is compared against the following representative coding and compression schemes: adaptive differential pulse code modulation (ADPCM) which reduces the transmitted data by a factor of two; linear predictive coding (LPC) with compression ratio of more than twelve to one; linear predictive coding algorithm using the United States Department of Defense Standard 1015 with compression ratio of 26:1; Global System Mobile (GSM) algorithm which reduces the transmitted data by a factor of five. The following parameters are compared: (i) quality of the reconstructed signal after decoding; (ii) compression ratios. (iii) signal to noise ratio (SNR); (iv) peak signal to noise ratio (PSNR); (v) normalized root mean square error (NRMSE).