Binaural sound localization using neural networks.
Rushby C. Craig, Timothy R. Anderson · The Journal of the Acoustical Society of America · 1992
Artificial neural networks (ANNs) were used as a tool to localize sound sources from simulated, binaural signals. The sound sources for the experiments were restricted to a circle of radius 9 ft, centered about the head and lying on the horizontal circle. Sound source positions were randomly selected from one of the 360, one-deg increments on the circle. Classes for the ANNs were created by dividing the circle into equally sized wedges, much like slices of a pie. The number of classes used in the experiments varied from 4 to 36. Two types of sound source signals were considered: tones and Gaussian noise. Three different feature sets were tried. Results will be presented that compare the performance of the three feature sets for each sound source type. The best feature set produced similar results in terms of localization accuracy on tones and Gaussian noise (over 91% for 18 classes). Observations were made of phenomena which also occur in human psychological experiments such as front–back confusions and increased difficulty in localization below 1500 Hz.