Dual-stream Speech Dereverberation Network Using Long-term and Short-term Cues

For reverberation, the current speech is usually influenced by the previous frames. Traditional neural network-based speech dereverberation (SD) methods directly map the current speech frame that only has short-term cues to clean speech or learn a mask, which can not utilize long-term information to remove late reverberation and further limit SD's ability. To address this issue, we propose a dual-stream speech dereverberation network (DualSDNet) using long-term and short-term cues. First, we analyze the effectiveness of using a finite impulse response (FIR) based on long-term information recorded filter by reverberation generation progress. Second, to make full use of both long-term and short-term information, we further design a dual-stream network, it can map both long and short speech to high-dimensional representation and pay more attention to a more helpful time index. The results of the REVERB Challenge data show that our DualSDNet consistently outperforms the state-of-the-art SD baselines.