# Show Reference: "Neural and statistical processing of spatial cues for sound source localisation"

Neural and statistical processing of spatial cues for sound source localisation In Neural Networks (IJCNN), The 2013 International Joint Conference on (August 2013), pp. 1-8, doi:10.1109/ijcnn.2013.6706886 by Jorge Davila-Chacon, Sven Magg, Jindong Liu, Stefan Wermter
@inproceedings{davila-chacon-et-al-2013,
author = {Davila-Chacon, Jorge and Magg, Sven and Liu, Jindong and Wermter, Stefan},
booktitle = {Neural Networks (IJCNN), The 2013 International Joint Conference on},
citeulike-article-id = {13338968},
doi = {10.1109/ijcnn.2013.6706886},
institution = {Dept. of Inf., Knowledge Technol. Group, Univ. of Hamburg, Hamburg, Germany},
isbn = {978-1-4673-6128-6},
issn = {2161-4393},
keywords = {biorobotic, ssl},
month = aug,
pages = {1--8},
posted-at = {2014-11-18 16:52:59},
priority = {2},
publisher = {IEEE},
title = {Neural and statistical processing of spatial cues for sound source localisation},
url = {http://dx.doi.org/10.1109/ijcnn.2013.6706886},
year = {2013}
}



The binaural sound source localization system based on the Liu et al. model does not on its own perform satisfactory on the iCub due to the robot's ego noise which is greater than that of the Nao (~60 dB compared to ~40 dB).

Dávila-Chacón et al. compare different methods for sound source localization on the iCub.

Among the methods compared by Dávila-Chacón et al. for sound source localization on the iCub are

• the Liu et al. system,
• the Liu et al. system with additional classifiers
• Cross-correlation.

Dávila-Chacón evaluated SOMs as a clustering layer on top of the MSO and LSO modules of the Liu et al. sound source localization system. On top of the clustering layer, they tried out a number of neural and statistical classification layers.

The result was inferior by a margin to the best methods they found.