A Composite T60 Regression and Classification Approach for Speech Dereverberation

TL;DR

This paper introduces a joint learning framework that combines T60 reverberation time estimation with dereverberation to improve speech quality in reverberant environments, leveraging acoustic environment information.

Contribution

It presents a novel composite approach that jointly estimates T60 and performs dereverberation, enhancing performance over existing methods.

Findings

Improved dereverberation performance in simulated environments.

Enhanced speech quality in real-world reverberant settings.

Effective integration of T60 estimation with dereverberation.

Abstract

Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time, T60, however, is an essential acoustic factor that reflects how reverberation may impact a signal. In this work, we propose to perform dereverberation while leveraging key acoustic information from the environment. More specifically, we develop a joint learning approach that uses a composite T60 module and a separate dereverberation module to simultaneously perform reverberation time estimation and dereverberation. The reverberation time module provides key features to the dereverberation module during fine tuning. We evaluate our approach in simulated and real environments, and compare against several approaches. The results show that this composite framework improves performance in environments.