Learning binaural sound localization through a neural network

Author

Palmieri, Francesco ; Datum, Michael ; Shah, Atul ; Moiseff, Andrew

Author_Institution

Connecticut Univ., Storrs, CT, USA

fYear

1991

fDate

4-5 Apr 1991

Firstpage

13

Lastpage

14

Abstract

A neural network system is implemented that uses binaural time/intensity cues for determining azimuth/elevation of a sound source. The system is designed to approximately mimic the sound localization behavior of the owl. The network is trained in a supervised learning mode. The errors between the estimated position (from the neural net) and the actual position (from an ideal optical sensor) are used to determine adaptively the synaptic connections. The learning paradigm used is the multiple extended Kalman algorithm, which allows training with no parameter adjustments

Keywords

hearing; learning systems; neural nets; binaural intensity cues; binaural sound localization; binaural time cues; ideal optical sensor; learning; multiple extended Kalman algorithm; neural network system; owl sound localisation behavior; parameter adjustments; sound source azimuth; sound source elevation; supervised learning mode; synaptic connections; Acoustic propagation; Artificial neural networks; Azimuth; Computational modeling; Computer simulation; Ear; Frequency estimation; Neural networks; Neurons; Supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Bioengineering Conference, 1991., Proceedings of the 1991 IEEE Seventeenth Annual Northeast

Conference_Location

Hartford, CT

Print_ISBN

0-7803-0030-0

Type

conf

DOI

10.1109/NEBC.1991.154557

Filename

154557