Neural network models of sound localization based on monaural information and based on binaural information

Abstract

Neural networks were trained with back propagation to localize ‘‘virtual’’ sounds that could originate from any of 24 azimuths (−165° to +180°) and 6 elevations (−36° to +54°). The sounds (clicks) were filtered with head related transfer functions and transformed into 22-point quarter-octave spectra. The networks were composed of 22 to 44 input nodes, followed by 50 hidden nodes and 30 output nodes (24 azimuth nodes and 6 elevation nodes). With the ‘‘binaural’’ configuration, the interaural delay and the interaural difference spectrum were provided as inputs to the network. With the ‘‘monaural’’ configuration, separate networks were trained with the left-ear and the right-ear spectra; a third, arbitrator, network learned to localize based on the output of these two monaural networks (i.e., the activation levels of their azimuth and elevation nodes). The monaural configuration did not allow for binaural interaction in the traditional sense. Both configurations achieved performance comparable to humans, suggesting that for these conditions either monaural cues or binaural cues are sufficient to explain human sound localization. [Work supported by NIH-DC-00786, AFOSR-91-0289, and AFOSR-Task 2313V3.]