Environmental Noise Classification for Multimedia Libraries

2005 ◽  
pp. 230-239 ◽  
Author(s):  
Stéphane Bressan ◽  
Boon Tiang Tan
Keyword(s):  
Environmental Noise ◽  
Noise Classification

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.

Predominant environmental noise classification over sound mixing based on source-specific dictionary

2016 ◽  
Vol 112 ◽  
pp. 171-180 ◽  
Author(s):  
María Guadalupe López-Pacheco ◽  
Luis Pastor Sánchez-Fernández ◽  
Herón Molina-Lozano ◽  
Luis Alejandro Sánchez-Pérez
Keyword(s):  
Environmental Noise ◽  
Noise Classification

scholarly journals Environmental Noise Classification Using Convolutional Neural Networks with Input Transform for Hearing Aids

2020 ◽  
Vol 17 (7) ◽  
pp. 2270 ◽  
Author(s):  
Gyuseok Park ◽  
Sangmin Lee
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hearing Aids ◽  
Classification Accuracy ◽  
Median Filter ◽  
Environmental Noise ◽  
Classification Algorithm ◽  
Correct Classification ◽  
Time Length ◽  
Noise Classification

Hearing aids are essential for people with hearing loss, and noise estimation and classification are some of the most important technologies used in devices. This paper presents an environmental noise classification algorithm for hearing aids that uses convolutional neural networks (CNNs) and image signals transformed from sound signals. The algorithm was developed using the data of ten types of noise acquired from living environments where such noises occur. Spectrogram images transformed from sound data are used as the input of the CNNs after processing of the images by a sharpening mask and median filter. The classification results of the proposed algorithm were compared with those of other noise classification methods. A maximum correct classification accuracy of 99.25% was achieved by the proposed algorithm for a spectrogram time length of 1 s, with the correct classification accuracy decreasing with increasing spectrogram time length up to 8 s. For a spectrogram time length of 8 s and using the sharpening mask and median filter, the classification accuracy was 98.73%, which is comparable with the 98.79% achieved by the conventional method for a time length of 1 s. The proposed hearing aid noise classification algorithm thus offers less computational complexity without compromising on performance.

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