On the compromise between noise reduction and speech/noise spatial information preservation in binaural speech enhancement

2021 ◽  
Vol 149 (5) ◽  
pp. 3151-3162
Author(s):  
Xin Leng ◽  
Jingdong Chen ◽  
Jacob Benesty
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Spatial Information ◽  
Information Preservation

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.

scholarly journals Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises

2020 ◽  
Vol 10 (17) ◽  
pp. 6077
Author(s):  
Gyuseok Park ◽  
Woohyeong Cho ◽  
Kyu-Sung Kim ◽  
Sangmin Lee
Keyword(s):  
Neural Networks ◽  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Deep Neural Networks ◽  
Hearing Aid ◽  
Environmental Noise ◽  
Speech Quality ◽  
Impaired Hearing ◽  
Noise Classification

Hearing aids are small electronic devices designed to improve hearing for persons with impaired hearing, using sophisticated audio signal processing algorithms and technologies. In general, the speech enhancement algorithms in hearing aids remove the environmental noise and enhance speech while still giving consideration to hearing characteristics and the environmental surroundings. In this study, a speech enhancement algorithm was proposed to improve speech quality in a hearing aid environment by applying noise reduction algorithms with deep neural network learning based on noise classification. In order to evaluate the speech enhancement in an actual hearing aid environment, ten types of noise were self-recorded and classified using convolutional neural networks. In addition, noise reduction for speech enhancement in the hearing aid were applied by deep neural networks based on the noise classification. As a result, the speech quality based on the speech enhancements removed using the deep neural networks—and associated environmental noise classification—exhibited a significant improvement over that of the conventional hearing aid algorithm. The improved speech quality was also evaluated by objective measure through the perceptual evaluation of speech quality score, the short-time objective intelligibility score, the overall quality composite measure, and the log likelihood ratio score.

scholarly journals Dual-Channel Speech Enhancement Based on Speech Presence Probability

2020 ◽  
Vol 9 (3) ◽  
pp. 2198-2200
Keyword(s):  
Noise Reduction ◽  
Speech Enhancement ◽  
Hearing Aids ◽  
Speech Processing ◽  
Speech Signal ◽  
Reduction Process ◽  
Coherent Noise ◽  
Residual Noise ◽  
Speech Distortion ◽  
Reduction Methods

This paper introduces technology to improve sound quality, which serves the needs of media and entertainment. Major challenging problem in the speech processing applications like mobile phones, hands-free phones, car communication, teleconference systems, hearing aids, voice coders, automatic speech recognition and forensics etc., is to eliminate the background noise. Speech enhancement algorithms are widely used for these applications in order to remove the noise from degraded speech in the noisy environment. Hence, the conventional noise reduction methods introduce more residual noise and speech distortion. So, it has been found that the noise reduction process is more effective to improve the speech quality but it affects the intelligibility of the clean speech signal. In this paper, we introduce a new model of coherence-based noise reduction method for the complex noise environment in which a target speech coexists with a coherent noise around. From the coherence model, the information of speech presence probability is added to better track noise variation accurately; and during the speech presence and speech absent period, adaptive coherence-based method is adjusted. The performance of suggested method is evaluated in condition of diffuse and real street noise, and it improves the speech signal quality less speech distortion and residual noise.

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