Deep Denoising for Hearing Aid Applications
This addresses noise reduction for hearing aid users, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of reducing non-stationary noises in hearing aids, proposing a deep learning-based denoising approach that outperformed a state-of-the-art baseline in objective metrics and subject tests.
Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.