Park Yeonseok, Choi Anthony, Kim Keonwook
Division of Electronics & Electrical Engineering, Dongguk University-Seoul, Seoul 04620, Korea.
Department of Electrical & Computer Engineering, Mercer University, 1501 Mercer University Drive, Macon, GA 31207, USA.
Sensors (Basel). 2020 Feb 10;20(3):925. doi: 10.3390/s20030925.
Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.
车载声源定位系统通过监测周围环境提供全面信息以改善驾驶条件。车辆的三维结构由于传播路径长且不均匀，阻碍了全向声音定位系统。在接收到的信号中，麦克风之间的飞行时间提供了定位声源的关键信息。本文提出了一种基于单模拟麦克风网络设计声音定位系统的新方法。本文涉及使用非参数同态反卷积估计两个麦克风的飞行时间。还建议使用尤尔-沃克（Yule-walker）、普罗尼（Prony）和斯蒂格利茨-麦克布赖德（Steiglitz-McBride）算法的参数方法来推导用于飞行时间估计的传播模型的系数值。对于20或更高的总体平均长度，非参数和斯蒂格利茨-麦克布赖德方法显示出显著较低的偏差和方差。尤尔-沃克和普罗尼算法随着总体平均长度的增加，统计性能逐渐提高。因此，非参数和参数同态反卷积很好地表示了飞行时间信息。在未来的工作中，具有不同长度的推导非参数和参数输出将作为基于机器学习或深度学习的完整定位系统的特征信息。
