@inproceedings{76830bc4c81642ef8933e689ff18fb91,
title = "A binaural sound localization system using deep convolutional neural networks",
abstract = "We propose a biologically inspired binaural sound localization system using a deep convolutional neural network (CNN) for reverberant environments. It utilizes a binaural Cascade of Asymmetric Resonators with Fast-Acting Compression (CAR-FAC) cochlear system to analyze binaural signals, a lateral inhibition function to sharpen temporal information of cochlear channels, and instantaneous correlation function on the two cochlear channels to encode binaural cues. The generated 2-D instantaneous correlation matrix (correlogram) encodes both interaural phase difference (IPD) cues and spectral information in a unified framework. Additionally, a sound onset detector is exploited to generate the correlograms only during sound onsets to remove interference from echoes. The onset correlograms are analyzed using a deep CNN for regression to the azimuthal angle of the sound. The proposed system was evaluated using experimental data in a reverberant environment, and displayed a root mean square localization error (RMSE) of 3.68{\~A}‚{\^A}° in the {\^a}ˆ{\textquoteright}90{\~A}‚{\^A}° to 90{\~A}‚{\^A}° range.",
keywords = "cochlea, directional hearing, neural networks (computer science), neuromorphics, reverberation, sound",
author = "Ying Xu and Saeed Afshar and Singh, {Ram Kuber} and Runchun Wang and Schaik, {Andre van} and Hamilton, {Tara Julia}",
year = "2019",
doi = "10.1109/ISCAS.2019.8702345",
language = "English",
isbn = "9781728103976",
publisher = "IEEE",
booktitle = "Proceedings of the 2019 IEEE International Symposium on Circuits and Systems (ISCAS 2019), 26-29 May 2019, Sapporo, Japan",
note = "IEEE International Symposium on Circuits and Systems ; Conference date: 26-05-2019",
}