engleski

Quality-Complexity Trade-off in Predictive LSF Quantization

In this paper several techniques are investigated for reduction of complexity and/or improving quality of a line spectrum frequencies (LSF) quantization based on switched prediction (SP) and vector quantization (VQ). For switched prediction, a higher number of prediction matrices is proposed. Quality of the quantized speech is improved by the prediction multi-candidate and delayed decision algorithm. It is shown that quantizers with delayed decision can save up to one bit still having similar or even lower complexity than the baseline quantizers with 2 switched matrices. By efficient implementation of prediction, lower complexity can be achieved through use of prediction matrices with reduced number of non-zero elements. By combining such sparse matrices and multiple prediction candidates, the best quality-complexity compromise quantizers can be obtained as demonstrated by experimental results.

LSf quantization; predictive vector quantization; delayed decision; sparse prediction matrices

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