A Complete Hardware Implementation of an Integrated Sound Localization and Classification System Based on Spiking Neural Networks

Several applications would emerge from the development of artificial systems able to accurately localize and identify sound sources. This paper proposes an integrated sound localization and classification system based on the human auditory system and a respective compact hardware implementation. The proposed models are based on spiking neurons, which are suitable for processing time series data, like sound signals, and can be easily implemented in hardware. The system uses two microphones, extracting the time difference between the two channels with a chain of coincidence detection spiking neurons. A spiking neural networks process the time-delay pattern, giving a single directional output. Simultaneously, an independent spiking neural network process the spectral information of on audio channel in order to classify the source. Experimental results show that a the proposed system could successfully locate and identify several sound sources in real time with high accuracy.