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Exploiting Spike-based Dynamics in a Silicon Cochlea for Speaker Identification


Chakrabartty, S; Liu, S C (2010). Exploiting Spike-based Dynamics in a Silicon Cochlea for Speaker Identification. In: Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium, Paris, France, 30 May 2010 - 2 June 2010, 513-516.

Abstract

Limit-cycle dynamics embedded in neuronal spike-trains can form robust representations for encoding auditory spectral features. In this paper, we present speaker identification experiments based on limit-cycle statistics that were computed using spike-trains obtained from a spike-based silicon cochlea. The features included in this study were: (a) spike-rate; (b) inter-spike-interval distribution; and (c) inter-spike-velocity features, which were then used to design a speaker identification system based on a Gini-support vector machine (SVM) classifier. The results show a strong correlation between the information contained in the spike-rate/interval features and the spike-velocity/acceleration features indicating redundant encoding of auditory features which could be important for achieving noise-robustness in real-world recording conditions.

Limit-cycle dynamics embedded in neuronal spike-trains can form robust representations for encoding auditory spectral features. In this paper, we present speaker identification experiments based on limit-cycle statistics that were computed using spike-trains obtained from a spike-based silicon cochlea. The features included in this study were: (a) spike-rate; (b) inter-spike-interval distribution; and (c) inter-spike-velocity features, which were then used to design a speaker identification system based on a Gini-support vector machine (SVM) classifier. The results show a strong correlation between the information contained in the spike-rate/interval features and the spike-velocity/acceleration features indicating redundant encoding of auditory features which could be important for achieving noise-robustness in real-world recording conditions.

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3 citations in Web of Science®
4 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:2 June 2010
Deposited On:02 Mar 2011 14:54
Last Modified:05 Apr 2016 14:51
Publisher:IEEE
Number of Pages:3
ISBN:978-1-4244-5308-5
Publisher DOI:10.1109/ISCAS.2010.5537578
Permanent URL: http://doi.org/10.5167/uzh-47188

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