Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance

Abstract

Speech compression techniques based on the traditional psychoacoustic model have been proposed by many researchers. We propose the Discrete Wavelet Transform (DWT) supported by the same psychoacoustic model for speech compression. This paper presents a traditional psychoacoustic model for processing equal partitions of the total bandwidth spectrum of audio signal frequencies in order to reduce redundancy by filtering out the tones and noise maskers in the speech signal. Here, uniform filter banks are used for efficient computation, for selection of appropriate threshold levels, and for better compression of Discrete Wavelet Transform coefficients. A Daubechies wavelet filter bank is nonlinear and asymmetric. It is equivalent to a cochlear filter in the human hearing system. The similarity between the Daubechies filter bank and our hearing system was the basis for developing a novel speech coder. Better performance in terms of the compression factor (CF) and the signal-to-noise ratio (SNR) resulted, as compared to the earlier methods.

Improving Accuracy of Isolated Word Recognition System by using Syllable Number Characteristics

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“…Different measuring parameters are used to calculate efficiency of audio encoding methods. These parameters are compression ratio, the ratio of signal to noise and ratio of peak signal to noise [7]- [8]. The convex set projection technique is proposed for improvement in recovery of speech spectral parameters [10].…”