外观
论文arXiv 2606.123392 个月前
Fast-SDE：混响环境中高效的单麦克风声源距离估计
原文链接：Fast-SDE: Efficient Single-Microphone Sound Source Distance Estimation in Reverberant Environments（arXiv 2606.12339 · 发布日期：2026-08-01）
Fast-SDE: Efficient Single-Microphone Sound Source Distance Estimation in Reverberant Environments
针对受限机器人上的声源距离估计，Fast-SDE以子带单麦克风框架在仿真和真实实验中验证。
核心要点
- 声源距离影响语音获取、理解及交互自然舒适度。
- 麦克风阵列需同步、标定且占用空间和计算资源，限制受限平台应用。
- 方法将频轴分解为多个子带，共享编码器学习紧凑表征，再回归距离。
- 仿真和真实世界实验显示方法优势，并已开源代码。
验证范围与局限：验证范围为仿真和真实世界实验，摘要未给出具体平台或任务。
英文摘要 (Abstract)
Sound source distance estimation (SDE) is a critical capability in human-robot interaction. An inappropriate interaction distance not only reduces the reliability of speech acquisition and understanding, but also compromises the naturalness and comfort of the interaction. Most existing SDE methods rely on microphone arrays, however, multi-microphone systems typically require careful hardware synchronization, geometric calibration, and additional space and computational resources, which limits applicability to size-constrained and computability-limited embodied platforms. To alleviate these issues, we propose Fast-SDE, a lightweight single-microphone SDE framework that is suited for deployment on robot platforms with limited computational resources and strict size constraints. Specifically, Fast-SDE employs a subband-based backbone that decomposes the frequency axis into multiple subbands, rather than processing the entire spectrum with a wide full-band backbone. A shared subband encoder then maps each subband to a compact latent representation and learns the relationship between acoustic structure and time-frequency patterns. Finally, a lightweight regression head converts the fused subband representations into the estimated distance. Extensive simulation and real-world experiments demonstrate the merits of the proposed method. To benefit the broader research community, we have open-sourced our code at https://github.com/JiangWAV/FAST-SDE.
作者：Jiang Wang (14683), Runwu Shi, Yaozhong Kang, Benjamin Yen, Takeshi Ashizawa, Kazuhiro Nakadai
来源
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