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Open Access

A Physiologically Inspired Method for Audio Classification

  • Sourabh Ravindran1Email author,
  • Kristopher Schlemmer1 and
  • David V. Anderson1
EURASIP Journal on Advances in Signal Processing20052005:561326

https://doi.org/10.1155/ASP.2005.1374

Received: 2 November 2003

Published: 21 June 2005

Abstract

We explore the use of physiologically inspired auditory features with both physiologically motivated and statistical audio classification methods. We use features derived from a biophysically defensible model of the early auditory system for audio classification using a neural network classifier. We also use a Gaussian-mixture-model (GMM)-based classifier for the purpose of comparison and show that the neural-network-based approach works better. Further, we use features from a more advanced model of the auditory system and show that the features extracted from this model of the primary auditory cortex perform better than the features from the early auditory stage. The features give good classification performance with only one-second data segments used for training and testing.

Keywords and phrases

auditory modelfeature extractionneural netsaudio classificationGaussian mixture models

Authors’ Affiliations

(1)
School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, USA

Copyright

© Ravindran et al. 2005

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