This study introduces a reformed Sinc-convolution (Sincconv) framework tailored for the encoder component of deep networks for speech enhancement (SE). The reformed Sincconv, based on parametrized sinc functions as band-pass filters, offers notable advantages in terms of training efficiency, filter diversity, and interpretability. The reformed Sinc-conv is evaluated in conjunction with various SE models, showcasing its ability to boost SE performance. Furthermore, the reformed Sincconv provides valuable insights into the specific frequency components that are prioritized in an SE scenario. This opens up a new direction of SE research and improving our knowledge of their operating dynamics.

翻译：本研究提出了一种改进的Sinc卷积（Sincconv）框架，专门用于深度网络语音增强（SE）中的编码器组件。该改进的Sincconv基于参数化sinc函数作为带通滤波器，在训练效率、滤波器多样性和可解释性方面具有显著优势。将改进的Sinc卷积与多种SE模型联合评估，展示了其提升SE性能的能力。此外，改进的Sincconv为SE场景中优先处理的特定频率成分提供了宝贵见解。这开辟了SE研究的新方向，并加深了对其运行动态的理解。