Neural Networks Hear You Loud And Clear: Hearing Loss Compensation Using Deep Neural Networks
arxiv(2024)
Abstract
This article investigates the use of deep neural networks (DNNs) for
hearing-loss compensation. Hearing loss is a prevalent issue affecting millions
of people worldwide, and conventional hearing aids have limitations in
providing satisfactory compensation. DNNs have shown remarkable performance in
various auditory tasks, including speech recognition, speaker identification,
and music classification. In this study, we propose a DNN-based approach for
hearing-loss compensation, which is trained on the outputs of hearing-impaired
and normal-hearing DNN-based auditory models in response to speech signals.
First, we introduce a framework for emulating auditory models using DNNs,
focusing on an auditory-nerve model in the auditory pathway. We propose a
linearization of the DNN-based approach, which we use to analyze the DNN-based
hearing-loss compensation. Additionally we develop a simple approach to choose
the acoustic center frequencies of the auditory model used for the compensation
strategy. Finally, we evaluate the DNN-based hearing-loss compensation
strategies using listening tests with hearing impaired listeners. The results
demonstrate that the proposed approach results in feasible hearing-loss
compensation strategies. Our proposed approach was shown to provide an increase
in speech intelligibility and was found to outperform a conventional approach
in terms of perceived speech quality.
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