A Priori SNR Computation for Speech Enhancement Based on Cepstral Envelope Estimation

In this contribution we present our latest investigations and analysis on a novel a priori SNR estimator for speech enhancement applications. It is based on a clean spectral envelope estimation with a deep neural network (DNN) in the cepstral domain. Furthermore, by integrating our cepstral excitation manipulation (CEM) approach into this framework, we obtain not only a smooth and natural background noise experience, but also achieve noise reduction between harmonics which is not possible with low-order models. We investigate the performance of the proposed approach in conjunction with three different spectral weighting rules and show improvement of more than 3.5 dB noise attenuation vs. the well-known decision-directed (DD) approach without a significant trade-off in speech distortion.

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