A physiologically inspired method for audio classification

Authors:
Sourabh Ravindran;Kristopher Schlemmer;David V. Anderson
Affiliations:
School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA;School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA;School of Electrical and Computer Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2005

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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.