  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.
