Literature DB >> 8675834

An artificial neural network for sound localization using binaural cues.

M S Datum1, F Palmieri, A Moiseff.   

Abstract

A three-layer neural network is used to estimate the direction of a sound source from the signals detected by two directional, spatially separate receivers. Although the implemented system does not require any specific knowledge about acoustical parameters or propagation properties, a model of the acoustical environment is used to generate simulated data for training the network. The neural network is trained according to the multiple extended Kalman algorithm (MEKA), which provides fast convergence and does not require intervention for adjustment of the learning parameters. Lower bounds on estimation are computed and compared with simulations using the neural network.

Mesh:

Year:  1996        PMID: 8675834     DOI: 10.1121/1.415854

Source DB:  PubMed          Journal:  J Acoust Soc Am        ISSN: 0001-4966            Impact factor:   1.840


  3 in total

1.  Sensitivity to spectral interaural intensity difference cues in space-specific neurons of the barn owl.

Authors:  B J Arthur
Journal:  J Comp Physiol A Neuroethol Sens Neural Behav Physiol       Date:  2003-12-11       Impact factor: 1.836

2.  A spiking neural network model of the medial superior olive using spike timing dependent plasticity for sound localization.

Authors:  Brendan Glackin; Julie A Wall; Thomas M McGinnity; Liam P Maguire; Liam J McDaid
Journal:  Front Comput Neurosci       Date:  2010-08-03       Impact factor: 2.380

3.  Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates.

Authors:  Juan Manuel Vera-Diaz; Daniel Pizarro; Javier Macias-Guarasa
Journal:  Sensors (Basel)       Date:  2018-10-12       Impact factor: 3.576

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.