Substitution of G.728 vocoder’s codebook search module with SOM array trained by PSO-optimized supervised algorithm

Abstract

Low delay-code excited linear prediction (LD-CELP) is an attractive algorithm in implementing vocoders in voice over Internet protocol networks. This algorithm has been proposed for the coding of speech at 16 kbps with toll quality. However, operation at transmission rates lower than 16 kbps is desirable, so that traffic can be accommodated during system overload conditions. In this paper, an array of self-organizing maps (SOMs) is employed instead of traditional codebook search module, recommended in ITU-T G.728, to determine the optimum index value of shape codebook. It is noted that a modified supervised training algorithm is used for SOMs in which some of the training parameters are optimized using particle swarm optimization (PSO) algorithm. Based on the occurrence frequency characteristics of codevectors, six bits for shape codebook and two bits for gain codebook are used in this work to produce a vocoder with lower bit rate as compared with traditional ITU-T G.728 vocoder. The performance comparison of the proposed SOM array trained by PSO-optimized supervised algorithm as the codebook search module in the structure of LD-CELP with a conventional implementation of LD-CELP coder shows that execution time of the algorithm is reduced up to 44 %. However, the degradation of voice quality in terms of mean opinion score, perceived evaluation of speech quality and segmental signal-to-noise ratio (SNRseg) is acceptable.