Spectral magnitude quantization based on linear transforms for 4 kb/s speech coding

This paper presents a matching pursuits sinusoidal speech coder which incorporates new techniques including a novel vector quantization (VQ) technique used for the weighted quantization of spectral magnitude vector, and an interframe quantization of spectral magnitudes using an interpolation matrix that minimize the weighted interpolation error. The paper describes a novel vector quantization technique, wherein the quantized vector is obtained by applying a linear transformation selected from a first codebook to a codevector selected from a second codebook. The transformation is selected from a family of linear transformations, represented by a matrix codebook. Vectors in the second codebook are called residual codevectors. In order to avoid high complexity during the search for the best linear transformation, each linear transformation is assigned a representative vector, such that the search can be done employing the representative vectors. The VQ design algorithm is based on joint optimization of the linear transformation and the residual codebooks. The introduced techniques are general enough to be used in any sinusoidal speech coding scheme. In this work we incorporated the techniques into the matching pursuits sinusoidal model to achieve high quality speech using sinusoidal speech coder at 4 kbps. Subjective tests indicate that the proposed coding model at 4 kbps has quality comparable to that of G.729 at 8kbps.