Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices

TL;DR

This paper introduces a customizable end-to-end online dereverberation method for hearing devices that directly optimizes the output, outperforming traditional DNN-supported WPE algorithms on a standard dataset.

Contribution

It presents a novel end-to-end optimization approach for online dereverberation that can be tailored to different hearing device needs, improving performance over existing methods.

Findings

Outperforms traditional DNN-supported WPE on WHAMR! dataset

Allows customization for different hearing device users

Demonstrates improved dereverberation quality

Abstract

This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density (PSD). Recently deep neural networks (DNNs) have been used for this task. However, these approaches optimize the PSD estimate which only indirectly affects the WPE output, thus potentially resulting in limited dereverberation. In this paper, we propose an end-to-end approach specialized for online processing, that directly optimizes the dereverberated output signal. In addition, we propose to adapt it to the needs of different types of hearing-device users by modifying the optimization target as well as the WPE algorithm characteristics used in training. We show that the proposed end-to-end approach outperforms the traditional and conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.