GMM-Based KLT-Domain Switched-Split Vector Quantization for LSF Coding

Abstract

For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.