Automatic classification of audio data using nonlinear neural response models

Physiologically inspired feature extraction for audio classification often uses simplified parametric models of auditory processing. We employ linear and nonlinear neuron models directly derived from neural responses in zebra finches as feature extraction front-ends. The most important features were identified using automatic feature selection techniques. This allows both a quantitative evaluation of neural features for sound classification tasks in terms of classification accuracy and a qualitative analysis of the auditory features that are most relevant. It turned out that a relatively small subpopulation of neural responses is sufficient to achieve reasonable classification performance. For linear as well as for nonlinear neuron models, we found three different shapes of spectro-temporal features to be archetypical. The relation of these to analytic approaches (such as Gabor filters) is discussed. The overall classification rates in a 6-class task reached up to 94% accuracy. Nonlinear models provided up to 15% benefit over linear models, indicating the importance of nonlinearities in classification with physiologically motivated features.