On improving the excitation signal in low-rate celp coding

Abstract

The main objective of this dissertation work is to bring the bit rate of a CELP coder to 4.8 kbits/s and lower while maintaining good speech quality. For this purpose, this dissertation work focuses on three major issues, that is, class-dependent modeling, and improving the weighting function and the excitation signal. For the class-dependent model we propose two new models which classify speech segments and use a different coding structure for each class. And, for the improved weighting function we propose a function which suppresses noise between harmonics of speech spectrum. Finally, for the improved excitation modeling we propose an excitation source with peaky pulse characteristic. First, we propose a CELP-based mixed source model (C-MSM) coder at 3 kbits/s. The coder classifies speech segments into three types: voiced, unvoiced and mixed. The class decision for each speech segment and the voiced/unvoiced determination for each frequency band are done by minimizing the perceptually weighted mean-squared error between an original and the corresponding reconstructed speech. The excitation for a voiced frame is generated from an adaptive source that is the output of a long-term predictor. The excitation for an unvoiced frame is generated from a stochastic source that is the scaled code vector of a Gaussian codebook. For a mixed frame the proposed coder uses a mixed source which combines a lowpass-filtered adaptive source and a highpass-filtered stochastic source. Simulation results show that the mixed source greatly reduces the buzzy quality associated with conventional LPC vocoders. According to listening tests, the proposed coder at 3 kbits/s is clearly superior to conventional LPC vocoders and is comparable to 4.8 kbits/s CELP coders. Second, we propose an improved weighting function in the error criterion. In general, the performance of a speech coder is heavily dependent on the selection of a weighting function in the error criterion. Previous methods of...