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Vector quantization of excitation gains in speech coding

Krishna, K and Murty, VLN and Ramakrishnan, KR (2001) Vector quantization of excitation gains in speech coding. In: Signal Processing, 81 (1). pp. 203-209.

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Abstract

In this paper, we consider vector quantization of excitation gains in code-excited linear predictive (CELP) speech coder using the average error in reconstruction of the excitation signal as the distortion measure and use the same measure to design the codebooks. We have derived a generalized Lloyd's algorithm (GLA) to design a codebook for quantization so that the average of the above criterion over the training vectors is minimized. We have also derived an algorithm, referred to as the Genetic GLA (GGLA), that can be shown to converge to the global optimum of the associated functional with probability one. The performance of ACELP using the codebooks obtained by the proposed algorithms is compared with that of the conjugate-structured ACELP-based ITU-T G.729 coder. Qualitative and quantitative comparisons show that their qualities are comparable.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to Elsevier Science.
Keywords: speech coding;vector quantization;CELP;generalized Lloyd's algorithm;genetic algorithm
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 11 Feb 2010 06:10
Last Modified: 19 Sep 2010 04:59
URI: http://eprints.iisc.ernet.in/id/eprint/17959

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