Dereverberation is often performed directly on the reverberant audio signal, without knowledge of the acoustic environment. Reverberation time,$T_{60}$, 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$T_{60}$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.

Reverberation time, T60, directly influences the amount of reverberation in a signal, and its direct estimation may help with dereverberation. Traditionally, T60estimation has been done using signal processing or probabilistic approaches, until recently where deep-learning approaches have been developed. Unfortunately, the appropriate loss function for training the network has not been adequately determined. In this paper, we propose a composite classification- and regression-based cost function for training a deep neural network that predicts T60for a variety of reverberant signals. We investigate pure-classification, pure-regression, and combined classification-regression based loss functions, where we additionally incorporate computational measures of success. Our results reveal that our composite loss function leads to the best performance as compared to other loss functions and comparison approaches. We also show that this combined loss function helps with generalization.

Dereverberation is often performed in the time-frequency domain using mostly deep learning approaches. Time-frequency domain processing, however, may not be necessary when reverberation is modeled by the convolution operation. In this paper, we investigate whether deverberation can be effectively performed in the frequency-domain by estimating the complex frequency response of a room impulse response. More specifically, we develop a joint learning framework that uses frequency-domain estimates of the late reverberant response to assist with estimating the direct and early response. We systematically compare our proposed approach to recent deep learning based approaches that operate in the time-frequency domain. The results show that frequency-domain processing is in fact possible and that it often outperforms time-frequency domain based approaches under different conditions.