Robust Linear Prediction Analysis for Low Bit-Rate Speech Coding
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Speech coding is a very important area of research in digital signal processing. It is a fundamental element of digital communications and has progressed at a fast pace in parallel to the increase of demands in telecommunication services and capabilities. Most of the speech coders reported in the literature are based on linear prediction (LP) analysis. Code Excited Linear Predictive (CELP) coder is a typical and popular example of this class of coders. This coder performs LP analysis of speech for extracting LP coefficients and employs an analysis-by-synthesis procedure to search a stochastic codebook to compute the excitation signal. The method used for performing LP analysis plays an important role in the design of a CELP coder. The autocorrelation method is conventionally used for LP analysis. Though this works reasonably well for noise-free (clean) speech, its performance goes down when signal is corrupted by noise. Spectral analysis of speech signals in noisy environments is an aspect of speech coding that deserves more attention. This dissertation studies the application of recently proposed robust LP analysis methods for estimating the power spectrum envelope of speech signals. These methods are the moving average, moving maximum and average threshold methods. The proposed methods will be compared to the more commonly used methods of LP analysis, such as the conventional autocorrelation method and the Spectral Envelope Estimation Vocoder (SEEVOC) method. The Linear Predictive Coding (LPC) spectrum calculated from these proposed methods are shown to be more robust. These methods work as well as the conventional methods when the speech signal is clean or has high signal-to-noise ratio. Also, these robust methods give less quantisation distortion than the conventional methods. The application of these robust methods for speech compression using the CELP coder provides better speech quality when compared to the conventional LP analysis methods.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Microelectronic Engineering
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