Environmental Noise Adaptable Hearing Aid using Deep Learning
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Soha A. Nossier Department of Biomedical Engineering, Medical Research Institute, Alexandria University, Egypt This email address is being protected from spambots. You need JavaScript enabled to view it. |
M. R. M. Rizk Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Egypt This email address is being protected from spambots. You need JavaScript enabled to view it. |
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Saleh El Shehaby Department of Biomedical Engineering, Medical Research Institute, Alexandria University, Egypt This email address is being protected from spambots. You need JavaScript enabled to view it. |
Nancy Diaa Moussa Department of Biomedical Engineering, Medical Research Institute, Alexandria University, Egypt This email address is being protected from spambots. You need JavaScript enabled to view it. |
Abstract: Speech de-nosing is one of the essential processes done inside hearing aids, and has recently shown a great improvement when applied using deep learning. However, when performing the speech de-noising for hearing aids, adding noise frequency classification stage is of a great importance, because of the different hearing loss types. Patients who suffer from sensorineural hearing loss have lower ability to hear specific range of frequencies over the others, so treating all the noise environments similarly will result in unsatisfying performance. In this paper, the idea of environmental adaptable hearing aid will be introduced. A hearing aid that can be programmed to multiply the background noise by a weight based on its frequency and 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 using diversity 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.
Keywords: Adaptable hearing aid, MFCC, neural networks, noise classification, speech enhancement.
Received December 1, 2020; accepted December 9, 2021