单麦克风实现移动机器人在混响环境下的实时声源定位
Single-Microphone-Based Sound Source Localization for Mobile Robots in Reverberant Environments
- 用单个麦克风提取混响信号的时间特征，轻量模型仅43k参数
- 结合扩展卡尔曼滤波，实现移动中机器人的在线声源定位
- 首个在移动机器人上实现单麦克风实时声源定位的工作
准确估计声源位置对机器人听觉至关重要。然而，现有声源定位方法通常依赖至少两个空间预配置的麦克风阵列，这限制了基于麦克风的机器人听觉系统的应用。为缓解这一挑战，我们提出一种基于安装在移动机器人上的单麦克风、适用于混响环境的在线声源定位方法。具体而言，我们设计了一个仅含43,000参数的轻量级神经网络模型，通过提取混响信号中的时间信息实现实时距离估计，并利用扩展卡尔曼滤波处理估计距离以完成在线声源定位。据我们所知，这是首个在移动机器人上实现单麦克风实时声源定位的工作，旨在填补该空白。大量实验验证了方法的有效性与优势。为促进研究社区发展，代码已开源：https://github.com/JiangWAV/single-mic-SSL。
原文摘要 · Abstract (English)
Accurately estimating sound source positions is crucial for robot audition. However, existing sound source localization methods typically rely on a microphone array with at least two spatially preconfigured microphones. This requirement hinders the applicability of microphone-based robot audition systems and technologies. To alleviate these challenges, we propose an online sound source localization method that uses a single microphone mounted on a mobile robot in reverberant environments. Specifically, we develop a lightweight neural network model with only 43k parameters to perform real-time distance estimation by extracting temporal information from reverberant signals. The estimated distances are then processed using an extended Kalman filter to achieve online sound source localization. To the best of our knowledge, this is the first work to achieve online sound source localization using a single microphone on a moving robot, a gap that we aim to fill in this work. Extensive experiments demonstrate the effectiveness and merits of our approach. To benefit the broader research community, we have open-sourced our code at https://github.com/JiangWAV/single-mic-SSL.
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