Deep Denoising for Hearing Aid Applications

TL;DR

This paper introduces a deep learning-based denoising method for hearing aids that improves noise reduction in real-time, low-latency applications, outperforming existing approaches on real-world noise data.

Contribution

A novel deep neural network approach for hearing aid noise reduction that operates in real-time with low latency and integrates with existing systems.

Findings

Outperforms state-of-the-art baseline in objective metrics

Effective in real-world noise scenarios

Compatible with hearing aid signal processing chains

Abstract

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.