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.