A classified split vector quantization of LFS parameters

Abstract Split vector quantization (SVQ) of LSF parameter suffers from huge complexity and storage requirements. Increasing the number of subvectors M results in a considerable decrease in both complexity and storage, but at the expense of rapid degradation in performance as M increases. To alleviate this problem, we propose classified SVQ (CSVQ), which uses class-dependent splitting and bit allocation schemes combined with a classified VQ structure. For practical applications, we designed two CSVQ structures. Experimental results have shown that both of the CSVQ schemes achieve nearly transparent quantization at 28 bits/frame while requiring much less complexity than the conventional SVQ.

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