Fusion of Amplitude and Complex Domains based on Deep Neural Networks for Speech Enhancement

2020 ◽  
Author(s):  
Mohammad Saeed Deylami ◽  
Sanaz Seyedin
Keyword(s):  
Neural Networks ◽  
Speech Enhancement ◽  
Deep Neural Networks

Text-informed speech enhancement with deep neural networks

2015 ◽  
Author(s):  
Keisuke Kinoshita ◽  
Marc Delcroix ◽  
Atsunori Ogawa ◽  
Tomohiro Nakatani
Keyword(s):  
Neural Networks ◽  
Speech Enhancement ◽  
Deep Neural Networks

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.

Experiments of Speech Enhancement Based on Deep Neural Networks in Far Field Scenarios

2016 ◽  
Author(s):  
Jacques Araujo ◽  
Walter Jr ◽  
André Almeida ◽  
Diego Viot
Keyword(s):  
Neural Networks ◽  
Speech Enhancement ◽  
Deep Neural Networks ◽  
Far Field
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