  The aim of speech enhancement is to improve speech signal quality and
intelligibility from a noisy microphone signal. In many applications, it is
crucial to enable processing with small computational complexity and minimal
requirements regarding access to future signal samples (look-ahead). This paper
presents signal-based causal DCCRN that improves online single-channel speech
enhancement by reducing the required look-ahead and the number of network
parameters. The proposed modifications include complex filtering of the signal,
application of overlapped-frame prediction, causal convolutions and
deconvolutions, and modification of the loss function. Results of performed
experiments indicate that the proposed model with overlapped signal prediction
and additional adjustments, achieves similar or better performance than the
original DCCRN in terms of various speech enhancement metrics, while it reduces
the latency and network parameter number by around 30%.
