DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement

Understand

Single-channel speech enhancement (SE) is an important task in speech processing.

• A widely used framework combines an analysis/synthesis filterbank with a mask prediction network, such as the Conv-TasNet architecture.
• In such systems, the denoising performance and computational efficiency are mainly affected by the structure of the mask prediction network.
• In this study, we aim to improve the sequential modeling ability of Conv-TasNet architectures by integrating Conformer layers into a new mask prediction network.