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The performance of the existing speech enhancement algorithms is not ideal in low signal-to-noise ratio (SNR) non-stationary noise environments. In order to resolve this problem, a novel speech enhancement algorithm based on multi-feature and adaptive mask with deep learning is presented in this paper. First, we construct a new feature called multi-resolution auditory cepstral coefficient (MRACC).
Correlations between Praat smoothed cepstral peak prominence (CPPS) and listener rating of overall severity for each speaking task. Solid lines indicate best-fit regression line, and dashed lines show 95% prediction intervals. The x-axis tick labels show the CPPS values corresponding to each y-axis tick label based on the regression line.
The correlation between Praat smoothed cepstral peak prominence (CPPS) and listener rating of overall severity for each speaking task. Solid lines indicate best-fit regression line, and dashed lines show 95% prediction intervals. The x-axis tick labels show the CPPS values corresponding to each y-axis tick label based on the regression line.
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