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.
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