Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array. The key of this model relies on the MTPC scheme, which encodes the interaural time difference (ITD) cues into spike patterns. This scheme naturally follows the functional structures of the human auditory localization system, rather than artificially computing of time difference of arrival. Besides, it highlights the advantages of SNN, such as event-driven and power efficiency. The MTPC is pipelined with two different SNN architectures, the convolutional SNN and recurrent SNN, by which it shows the applicability to various SNNs. This proposal is evaluated by the microphone collected location-dependent acoustic data, in a real-world environment with noise, obstruction, reflection, or other affects. The experiment results show a mean error azimuth of 1~3 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.

中文翻译：

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基于尖峰神经网络的耳间时差多音相位编码（MTPC）

受哺乳动物听觉定位途径的启发，在本文中，我们提出了一种基于纯尖峰神经网络 (SNN) 的计算模型，用于在嘈杂的现实世界环境中进行精确的声音定位，并在带有麦克风的实时机器人系统中实现该算法大批。该模型的关键依赖于 MTPC 方案，该方案将耳间时间差 (ITD) 线索编码为尖峰模式。该方案自然遵循人类听觉定位系统的功能结构，而不是人工计算到达时间差。此外，它还突出了 SNN 的优势，例如事件驱动和功率效率。MTPC 使用两种不同的 SNN 架构进行流水线化，即卷积 SNN 和循环 SNN，通过它们显示了对各种 SNN 的适用性。在具有噪声、障碍物、反射或其他影响的真实环境中，通过麦克风收集的位置相关声学数据来评估该提议。实验结果显示平均误差方位角为 1~3 度，超过了其他生物学上合理的神经形态声源定位方法的准确性。