Enhancing Subband Speech Processing: Integrating Multi-View Attention Module into Inter-SubNet for Superior Speech Enhancement

The Inter-SubNet speechenhancement network improves subband interaction by enabling the exchange of complementary information across frequency bands, ensuring robust feature refinement while significantly reducing computational load through lightweight, subband-specific modules. Despite its compact design, it outperforms state-of-the-art models such as FullSubNet, FullSubNet+, Conv-TasNet, and DCCRN+, offering a highly efficient yet powerful solution. To further enhance its performance, we propose integrating a Multi-view Attention (MA) module as a front-end or intermediate component. The MA module utilizes attention mechanisms across channel, global, and local views to emphasize critical features, ensuring comprehensive speech signal processing. Evaluations on the VoiceBank-DEMAND dataset show that incorporating the MA module significantly improves metrics like SI-SNR, PESQ, and STOI, demonstrating its effectiveness in enhancing subband feature extraction and overall speech enhancement performance.