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The reverberant speech segregation is a basic problem in speech enhancement and automatic speech recognition. Based on the deep neural networks (DNN), a novel binaural speech segregation method is proposed. The binaural feature is extracted and used as the cue to train a DNN with a ideal parameter mask. The trained DNN is used to distinguish the target speech and noise, and output the estimated parameter...
Robust speech enhancement is a challenge task, especially in noisy environments. The deep neural network has shown good performance on binaural speech enhancement with various speakers at a same distance. As binaural cues are based on the locations of sound sources, this paper analyze the performance of binaural deep neural network with different distances. The theoretical derivation and experiment...
Speech signal degradation in real environments mainly results from room reverberation and concurrent noise. While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. We treat the binaural segregation problem as binary classification, and employ deep neural networks (DNNs) for the classification task...
This paper addresses the problem of close talk speech enhancement as a binary classification using dual microphones features in noisy and reverberant environments. In this work, we investigate a speech segregation framework, in which deep neural networks (DNN) are employed as a mechanism to find the robustness classifier from two microphones inputs. The paper reports the successful attempt to use...
Monaural speech segregation from complex concurrent noise is an extremely challenging problem; binary mask is a method to solve this problem, however, the performance of binary mask is limited by remaining the noise in the result. In this paper, an algorithm integrated Spectral Subtraction and binary masking for speech separation and enhancement was proposed. It follows the framework of computational...
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