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

arXiv: https://arxiv.org/abs/2204.02694 · HTML: https://mlsanity.com/paper/2204.02694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [WHAMR!: Noisy and Reverberant Single-Channel Speech Separation](https://mlsanity.com/paper/1910.10279/) (2019)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend](https://mlsanity.com/paper/2102.11525/) (2021)
- [Coherent-to-Diffuse Power Ratio Estimation for Dereverberation](https://mlsanity.com/paper/1502.03784/) (2015)

## Built on by (most cited first, 1 of 1)

- [Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation](https://mlsanity.com/paper/2303.00529/) (2023)

## Abstract (arXiv)

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.