DYNAMIC SLIDING WINDOW FOR REALTIME DENOISING NETWORKS

Realtime speech denoising has been long studied. Almost all existing methods process the incoming data stream using a sliding window of fixed-size. This approach is often used as granted. Yet, we show that the use of fixed-size sliding window may lead to an accumulating lag, especially in presence of other background computing processes that may occupy CPU resources. In response, we propose a new sliding window strategy and a lightweight neural network to leverage it. Our experiments show that the proposed approach achieves denoising quality on a par with the state-of-the-art realtime denoising models. More importantly, our approach is faster, maintaining a stable realtime performance even when the available computing power fluctuates.