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Deep neural networks (DNNs) have recently became a viable methodology for single microphone speech enhancement. The most common approach, is to feed the noisy speech features into a fully-connected DNN to either directly enhance the speech signal or to infer a mask which can be used for the speech enhancement. In this case, one network has to deal with the large variability of the speech signal. Most...
Estimating the noise power spectral density (PSD) is essential for single channel speech enhancement algorithms. In this paper, we propose a noise PSD estimation approach based on regional statistics. The proposed regional statistics consist of four features representing the statistics of the past and present periodograms in a short-time period. We show that these features are efficient in characterizing...
Speech signals recorded with a distant microphone usually contain reverberation and noise, which degrade the fidelity and intelligibility of speech, and the recognition performance of automatic speech recognition systems. In E. Habets (2005) presented a multi-microphone speech dereverberation algorithm to suppress late reverberation in a noise-free environment. In this paper we show how an estimate...
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