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Improving the perceptual quality of speech signals is a key yet challenging problem for many real world applications. In this paper, we propose a perceptually motivated approach based on deep neural networks (DNNs) for speech enhancement task. The proposed approach take into consider the masking properties of the human auditory system and reduces the perceptual effect of the residual noise. Given...
This paper investigates the application of the so-called reduced-rank principle to the problem of single-channel noise reduction. Under the reduced-rank framework, we develop reduced-rank Wiener and tradeoff filters. In comparison to the classical Wiener filter, the reduced-rank Wiener one can provide a degree of freedom to control the compromise between noise reduction and speech distortion. Similarly,...
Linear equalizers (LEs) have been widely adopted for practical systems due to their low computational complexity. However, it is also well known that LEs provide inferior performance relative to a maximum-likelihood equalizer (MLE) or other near-MLEs, because LEs usually cannot collect the diversity order enabled by the transmitter and at the same time they lose mutual information. More importantly,...
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