Research On Low Bit Rates Waveform Interpolation Speech Coding Based On Singular Value Decomposition

Recently, there is more and more demand for wireless communication and speech storage. With the development of communication and Internet technology, lots of new voice-based services have appeared. Therefore, speech coding is still the key technique in the field of communication. The goal of this thesis is to develop a kind of 2.4kbps WI speech coder based on Waveform Interpolation (WI) coding, and have it simulated on computer by C programming language. In traditional WI coder, the residual signal is represented by an evolving characteristic waveform. The CW is decomposed into a slowly evolving waveform (SEW) and a rapidly evolving waveform (REW), representing the quasi-periodic and non-periodic components of speech signal, respectively. The decomposition of CW is accompolished along the time axis by non-causality linear FIR low-pass filter. Therefore, an additional delay of one frame is added and it's hard to control the decomposed precision. According to above problems, the Singular Value Decomposition (SVD) is proposed in this thesis to decompose characteristic Waveform (CW), which reduced the delay of speech coder and improved the precision of decomposition. Firstly, in order to reduced the computational complexity, the magnitude spectrum of CW is divided into basic matrix,transitional matrix and supplemental matrix. Then, the basic matrix is decomposed by SVD, which is represented by underlying matrix according to the requirement of bit rates; and the transitional matrix and the supplemental matrix are approximated by caculating the mean of each column and Discrete Cosine Transform (DCT), respectively. Finally, the underlying matrix,the DCT coefficients and mean-value are vector quantized. Subjective A/B listening tests indicated that the reconstructed speech quality of the 2.4kbps SVD-WI codec is a little better than that of 2.4kbps MELP coder.