Dereverberation based on bin-wise temporal variations of complex spectrogram

Humans analyze sounds not only based on their frequency contents, but also on the temporal variations of the frequency contents. Inspired by auditory perception, we propose a deep neural network (DNN) based dereverberation algorithm in the rate domain, which presents the temporal variations of frequency contents, in this paper. We show convolutional noise in the time domain can be approximated to multiplicative noise in the rate domain. To remove the multiplicative noise, we adopt the rate-domain complex-valued ideal ratio mask (RDcIRM) as the training target of the DNN. Simulation results show that the proposed rate-domain DNN algorithm is more capable of recovering high-intelligible and high-quality speech from reverberant speech than the compared state-of-the-art dereverberation algorithm. Hence, it is highly suitable for speech applications involving human listeners.