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%.

翻译：语音增强的目标是从带噪麦克风信号中提升语音信号的质量和可懂度。在许多应用中，以较小的计算复杂度和对未来信号样本（前瞻）的极低需求进行处理至关重要。本文提出了一种基于信号的因果DCCRN，通过减少所需的前瞻和网络参数数量，改进了在线单通道语音增强。所提出的改进包括对信号进行复数滤波、应用重叠帧预测、因果卷积与反卷积，以及损失函数的修改。实验结果表明，所提出的具有重叠信号预测及其他调整的模型，在多种语音增强指标上达到了与原始DCCRN相似或更优的性能，同时将延迟和网络参数数量减少了约30%。