Perceptual irrelevancy removal in narrowband speech coding

A masking model originally designed for audio signals is applied to narrowband speech. The model is used to detect and remove the perceptually irrelevant simultaneously masked frequency components of a speech signal. Objective measurements have shown that the modified speech signal can be coded more efficiently than the original signal. Furthermore, it has been confirmed through perceptual evaluation that the removal of these frequency components does not cause significant degradation of the speech quality but rather, it has consistently improved the output quality of two standardized speech codecs. Thus, the proposed irrelevancy removal technique can be used at the front end of a speech coder to achieve enhanced coding efficiency.

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