Improving Deep Neural Network Based Speech Enhancement in Low SNR Environments

2015 ◽  
pp. 75-82 ◽  
Author(s):  
Tian Gao ◽  
Jun Du ◽  
Yong Xu ◽  
Cong Liu ◽  
Li-Rong Dai ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Deep Neural Network ◽  
Low Snr

scholarly journals An Improved Fully Convolutional Network Based on Post-Processing with Global Variance Equalization and Noise-Aware Training for Speech Enhancement

2021 ◽  
Vol 25 (1) ◽  
pp. 130-137
Author(s):  
Wenlong Li ◽  
◽  
Kaoru Hirota ◽  
Yaping Dai ◽  
Zhiyang Jia
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Deep Neural Network ◽  
Voice Conversion ◽  
Post Processing ◽  
Generalization Capability ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Subjective Score ◽  
Model Training

An improved fully convolutional network based on post-processing with global variance (GV) equalization and noise-aware training (PN-FCN) for speech enhancement model is proposed. It aims at reducing the complexity of the speech improvement system, and it solves overly smooth speech signal spectrogram problem and poor generalization capability. The PN-FCN is fed with the noisy speech samples augmented with an estimate of the noise. In this way, the PN-FCN uses additional online noise information to better predict the clean speech. Besides, PN-FCN uses the global variance information, which improve the subjective score in a voice conversion task. Finally, the proposed framework adopts FCN, and the number of parameters is one-seventh of deep neural network (DNN). Results of experiments on the Valentini-Botinhaos dataset demonstrate that the proposed framework achieves improvements in both denoising effect and model training speed.

Supervised speech enhancement based on deep neural network

2019 ◽  
Vol 37 (4) ◽  
pp. 5187-5201 ◽  
Author(s):  
Nasir Saleem ◽  
Muhammad Irfan Khattak ◽  
Abdul Baser Qazi
Keyword(s):  
Neural Network ◽  
Speech Enhancement ◽  
Deep Neural Network

scholarly journals Environmental Attention-Guided Branchy Neural Network for Speech Enhancement

2020 ◽  
Vol 10 (3) ◽  
pp. 1167 ◽  
Author(s):  
Lu Zhang ◽  
Mingjiang Wang ◽  
Qiquan Zhang ◽  
Ming Liu
Keyword(s):  
Neural Network ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Deep Neural Network ◽  
Experimental Results ◽  
Noisy Environments ◽  
Neural Structure ◽  
Noise Interference ◽  
Noise Type ◽  
Speech Reconstruction

The performance of speech enhancement algorithms can be further improved by considering the application scenarios of speech products. In this paper, we propose an attention-based branchy neural network framework by incorporating the prior environmental information for noise reduction. In the whole denoising framework, first, an environment classification network is trained to distinguish the noise type of each noisy speech frame. Guided by this classification network, the denoising network gradually learns respective noise reduction abilities in different branches. Unlike most deep neural network (DNN)-based methods, which learn speech reconstruction capabilities with a common neural structure from all training noises, the proposed branchy model obtains greater performance benefits from the specially trained branches of prior known noise interference types. Experimental results show that the proposed branchy DNN model not only preserved better enhanced speech quality and intelligibility in seen noisy environments, but also obtained good generalization in unseen noisy environments.

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