论文标题
通过峰值神经网络的多音调相位差异的相位编码（MTPC）
Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
论文作者
论文摘要
受哺乳动物的听觉定位途径的启发，在本文中，我们提出了一个基于噪音的纯粹的尖峰神经网络（SNN）计算模型，用于在嘈杂的现实世界中的精确声音本地化，并在带有微台面阵列的实时机器人系统中实现该算法。该模型的关键依赖于MTPC方案，该方案将编码在尖峰模式中编码的时间差（ITD）线索。该方案自然遵循人类听觉定位系统的功能结构，而不是人为地计算到达的时间差。此外，它突出了SNN的优势，例如事件驱动和功率效率。 MTPC使用两个不同的SNN架构（卷积SNN和经常性SNN）管道上，通过它们显示对各种SNN的适用性。该提案通过麦克风收集的位置依赖性声学数据评估，在现实世界中具有噪声，阻塞，反射或其他影响的真实环境中。实验结果表明，平均误差方位角为1〜3度，这超过了其他生物学上合理的神经形态方法的准确性。
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.
