Improvements of the Beta-Order Minimum Mean-Square Error (MMSE) Spectral Amplitude Estimator using Chi Priors

نویسندگان

  • Marek B. Trawicki
  • Michael T. Johnson
چکیده

In this paper, the authors propose the Beta-Order Minimum Mean-Square Error (MMSE) Spectral Amplitude estimator with Chi statistical models for the speech priors. The new estimator incorporates both a shape parameter on the distribution and cost function parameter. The performance of the MMSE Beta-Order Spectral Amplitude estimator with Chi speech prior is evaluated using the Segmental Signal-to-Noise Ratio (SSNR) and Perceptual Evaluation of Speech Quality (PESQ) objective quality measures. From the experimental results, the new estimator provides gains of 0-3 dB and 0-0.03 in SSNR and PESQ improvements over the corresponding MMSE Beta-Order MMSE Spectral Amplitude estimator with the standard Rayleigh statistical models for the speech prior.

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تاریخ انتشار 2012