Environmental Noise Adaptable Hearing Aid using Deep Learning

The international arab journal of information technology

article

M. R. M. Rizk¹  Saleh El Shehaby²  Nancy Diaa Moussa² 

[1] Department of Electrical Engineering, Faculty of Engineering, Alexandria University;Department of Biomedical Engineering, Medical Research Institute, Alexandria University

关键词: Adaptable hearing aid;    MFCC;    neural networks;    noise classification;    speech enhancement;   

DOI  :  10.34028/iajit/19/5/15

学科分类：计算机科学（综合）

来源: Zarqa University

【 摘 要 】

Speech de-nosing is one of the essential processes done inside hearing aids, and has recently shown a greatimprovement when applied using deep learning. However, when performing the speech de-noising for hearing aids, adding noisefrequency classification stage is of a great importance, because of the different hearing loss types. Patients who suffer fromsensorineural hearing loss have lower ability to hear specific range of frequencies over the others, so treating all the noiseenvironments similarly will result in unsatisfying performance. In this paper, the idea of environmental adaptable hearing aidwill be introduced. A hearing aid that can be programmed to multiply the background noise by a weight based on its frequencyand importance, to match the case and needs of each patient. Furthermore, a more generalized Deep Neural Network (DNN)for speech enhancement will be presented, by training the network on a diversity of languages, instead of only the target language.The results show that the learning process of DNN for speech enhancement is more efficient when training the network usingdiversity of languages. Moreover, the idea of adaptable hearing aid is shown to be promising and achieved 70% overall accuracy.This accuracy can be improved using a larger environmental noise dataset.

【 授权许可】

Unknown