Sound Source Localization through 8 MEMS Microphones Array Using a Sand-Scorpion-Inspired Spiking Neural Network

Abstract

Sand-scorpions and many other arachnids perceive their environment by using their feet to sense ground waves. They are able to determine amplitudes the size of an atom and locate the acoustic stimuli with an accuracy of within 13° based on their neuronal anatomy. We present here a prototype sound source localization system, inspired from this impressive performance. The system presented utilizes custom-built hardware with eight MEMS microphones, one for each foot, to acquire the acoustic scene, and a spiking neural model to localize the sound source. The current implementation shows smaller localization error than those observed in nature.

Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays

“…In 2010, Byoung-gi Lee et al extended the mutual angle correlation function according to the cross-correlation function and used the k-means++ algorithm [4] . In 2016, Christoph Beck et al used the heuristic pulse neural network to build a sound source localization system composed of 8 electromechanical microphones, which had a very high localization accuracy [5] . In the same year, Daniele Salvati et al proposed to use the RBF kernel support vector machine to construct the weighted minimum variance-free response beamformer, which could effectively deal with the single sound source localization in the near field [6] .…”