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.

翻译：去混响通常直接对混响音频信号进行处理，而不考虑声学环境信息。然而，混响时间T60作为反映混响对信号影响程度的关键声学参数，本文提出在利用环境关键声学信息的同时进行去混响处理。具体而言，我们开发了一种联合学习方法，通过复合T60模块与独立去混响模块同步实现混响时间估计与去混响功能。在微调阶段，混响时间模块为去混响模块提供关键特征。我们在仿真环境和真实环境下对方法进行评估，并与多种现有方法进行对比。结果表明，该复合框架能够提升各类环境下的处理性能。