Skip to Main Content
In this paper, voice signal compression (VSC) is a technique that is used to convert the voice signal into encoded form and when compression is required, it can be decoded at the closest approximation value of the original signal. This work present a new algorithm to compress voice signals by using an ldquoadaptive wavelet packet decomposition and psychoacoustic modelrdquo. The main goals of this paper are: i) transparent compression (proposed 48% to 50%) of high quality voice signal at about 45 kbps with same extension (i.e. .wav to .wav) ii) To evaluate compressed voice signal with original voice signal with the help of distortion analysis and frequency spectrum. iii) To reduce the maximum noise from the compressed file and calculate the SNR (signal to noise ratio). To do this, a filter bank is used according to psychoacoustic model criteria and computational complexity of the decoder. The bit allocation method is used that also takes the input from Psychoacoustic model. Filter bank structure generates quality of performance in the form of subband perceptual rate which is computed in the form of perceptual entropy (PE). Output can get best value reconstruction possible considering the size of the output existing at the encoder. The result is a variable-rate compression scheme for high-quality voice signal.This work is well suited to high-quality voice signal transfer for Internet and storage applications.