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.
Highlights
Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal
We present features extracted from a model of the early auditory system that have been shown to be robust to noise [1, 2]
The results for all the features were better with an NN classifier as compared to a GMM classifier
Summary
Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal. In an attempt to bridge the gap in performance between humans and machines, there has been an increased effort to study and model physiological processes. It is likely the case that signal features and biological processing techniques evolved together and are complementary or well matched. It is precisely because of this reason that modeling the feature extraction processes should go hand in hand with the modeling of the processes that use these features
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