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The a Priori SNR Estimator Based on Cepstral Processing

For single-channel speech enhancement systems, the a priori SNR is a key parameter for Wiener-type algorithms. The a priori SNR estimators can reduce the noise efficiently when the noise power spectral density (NPSD) can be estimated accurately. However, when the NPSD is overestimated/underestimated, the a priori SNR may lead to the speech distortion and the residual noise. To solve this problem, this paper proposes to estimate the a priori SNR based on cepstral processing, which not only can suppress harmonic speech components in the noisy speech segments, but also can reduce strong noise components in noise-only segments. Simulation results show that the proposed algorithm has better performance than the traditional DD and Plapous’s two-step algorithms.

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