Speech Compression for Better Audibility using Wavelet Transformation with Adaptive Kalman Filtering

Abstract

In mobile communication systems, service providers are continuously met with the challenge of accommodating more users within a limited allocated bandwidth. For this reason, manufactures and service providers are continuously in search of low bit-rate speech coders that deliver toll-quality speech. This paper deals with speech compression based on discrete wavelet transforms and Adaptive Kalman filter. We used English words for this experiment. We could successfully compressed and reconstructed the words with perfect audibility by using above technique. Speech compression is the technology of converting human speech into an efficiently encoded representation that can later be decoded to produce a close approximation of the original signal. The wavelet transform of a signal decomposes the original signal into wavelets coefficients at different scales and positions. These coefficients represent the signal in the wavelet domain and all data operations can be performed using the corresponding wavelet coefficients. In this paper Code was simulated using MATLAB. The result obtained from Wavelet Coding was compared with Adaptive Kalman with Wavelet Coding. From the results we noticed that the performance of Wavelet Coding with Adaptive Kalman Filter is better than wavelet transform.