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 novel 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 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 both performance and computational efficiency when compared with existing state-of-the-art BWE methods.

翻译：语音带宽扩展（BWE）在实际通信系统中提升感知语音质量方面已展现出优异性能。现有BWE研究主要聚焦于固定上采样率，而忽略了实际捕获音频的有效带宽可能因采集设备和传输条件的差异而频繁波动。本文提出一种名为BAE-Net的新型流式自适应带宽扩展方法，适用于处理有效带宽未知且动态变化的低分辨率语音。为盲恢复高频语音信号的幅度和相位信息，我们设计了融合幅度修复与相位精化的双流架构。针对边缘设备应用场景，本文进一步提出轻量化流式高效框架BAE-NET-lite。定量结果表明，与现有最先进的BWE方法相比，BAE-Net在性能和计算效率方面均具有显著优势。