Composer classification based on temporal coding in adaptive spiking neural networks

N. Chaitanya Prasad, Krishnakant Saboo, Bipin Rajendran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We develop a spiking neural network (SNN) based implementation of a feature based on melodic interval prevalence for composer classification of a musical composition. The network has an adaptive spike-time based weight update rule which accurately captures the classification feature. Compared to the non-neural network based baseline implementation, the SNN implementation has a performance of 95.4%. When the songs are corrupted by gaussian additive noise, the relative degradation in performance of our algorithm is lesser than what is observed in the baseline algorithm.We also demonstrate that the performance degradation of our algorithm is minimal over a wide range of perturbations in the internal parameters of our circuit, demonstrating the power of adaptive SNNs to perform complex discrimination tasks in a fault-tolerant manner.

Original languageEnglish (US)
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
StatePublished - Sep 28 2015
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: Jul 12 2015Jul 17 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2015-September

Other

OtherInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period7/12/157/17/15

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

Keywords

  • Artificial neural networks

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