BAE-Net: a Low Complexity and High Fidelity Bandwidth-Adaptive Neural Network for Speech Super-Resolution

Guochen Yu, Xiguang Zheng, Nan Li, Runqiang Han, Chengshi Zheng, Chen Zhang, Chao Zhou, Qi Huang, Bing Yu

Abstract: Speech bandwidth extension (BWE) has demonstrated promising performance in enhancing the perceptual speech quality in real communication systems. Most existing BWE researches primarily focus on fixed upsampling ratios, disregarding the fact that the effective bandwidth of captured audio may fluctuate frequently due to various capturing devices and transmission conditions. In this paper, we propose a streaming adaptive bandwidth extension solution dubbed BAE-Net, which is suitable to handle the low-resolution speech with unknown and varying effective bandwidth. To address the challenges of recovering both the high-frequency magnitude and phase components of the speech content blindly, we devise a dual-stream architecture that incorporates the magnitude inpainting and phase refinement. For potential applications on edge devices, this paper also introduces BAE-NET-lite, which is a lightweight, streaming and efficient framework. Quantitative results demonstrate the superiority of BAE-Net in terms of performance and computational efficiency when compared with existing state-of-the-art BWE methods.

External IDs:dblp:conf/icassp/YuZLHZZZHY24