A Machine Hearing System for Binaural Sound Localization based on Instantaneous Correlation
Ying Xu, Saeed Afshar, Ram Kuber Singh, Tara Julia Hamilton, Runchun Wang, André van Schaik · 2018
We propose a biologically inspired binaural sound localization system for reverberant environments. It uses two 100-channel cochlear models to analyze binaural signals, and each channel of the left cochlea is compared with each channel of the right cochlea in parallel to generate a 2-D instantaneous correlation matrix (correlogram). The correlogram encodes both binaural cues and spectral information in a unified framework. A sound onset detector is used to generate the correlogram only during the sound onsets, and the onset correlogram is analyzed using a linear regression approach as well as an extreme learning machine (ELM). The proposed system is evaluated using experimental data in reverberation environments, and we obtained an average absolute error of 16.5° for linear regression and 12.8° for ELM regression in the -90° to 90° range.