Show Reference: "Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation"

Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation In Artificial Neural Networks and Machine Learning – ICANN 2012, Vol. 7552 (2012), pp. 239-246, doi:10.1007/978-3-642-33269-2_31 by Jorge Dávila-Chacón, Stefan Heinrich, Jindong Liu, Stefan Wermter edited by Alessandro E. P. Villa, Wlodzislaw Duch, Péter Érdi, Francesco Masulli, Günther Palm
    abstract = {This paper presents a spiking neural network ({SNN}) for binaural sound source localisation ({SSL}). The cues used for {SSL} were the interaural time ({ITD}) and level ({ILD}) differences. {ITDs} and {ILDs} were extracted with models of the medial superior olive ({MSO}) and the lateral superior olive ({LSO}). The {MSO} and {LSO} outputs were integrated in a model of the inferior colliculus ({IC}). The connection weights between the {MSO} and {LSO} neurons to the {IC} neurons were estimated using Bayesian inference. This inference process allowed the algorithm to perform robustly on a robot with \~{}{40,dB} of ego-noise. The results showed that the algorithm is capable of differentiating sounds with an accuracy of 15°.},
    author = {D\'{a}vila-Chac\'{o}n, Jorge and Heinrich, Stefan and Liu, Jindong and Wermter, Stefan},
    booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2012},
    citeulike-article-id = {11870927},
    citeulike-linkout-0 = {\_31},
    citeulike-linkout-1 = {\_31},
    doi = {10.1007/978-3-642-33269-2\_31},
    editor = {Villa, Alessandro E. P. and Duch, W{\l}odzis{\l}aw and \'{E}rdi, P\'{e}ter and Masulli, Francesco and Palm, G\"{u}nther},
    keywords = {ann, auditory, biomimetic, experiment, ic, learning, localization, robot, spiking},
    pages = {239--246},
    posted-at = {2013-01-09 11:03:41},
    priority = {2},
    publisher = {Springer Berlin Heidelberg},
    series = {Lecture Notes in Computer Science},
    title = {Biomimetic Binaural Sound Source Localisation with {Ego-Noise} Cancellation},
    url = {\_31},
    volume = {7552},
    year = {2012}

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Dávila-Chacón et al. show that the Liu et al. model of natural binaural sound source localization can be transferred to the Nao robot and there shows significant resilience to noise.

Their system can localize sounds with a spatial resolution of 15 degrees.