AUTOMATIC CLASSIFICATION OF AUDIO DATA USING NONLINEAR NEURAL RESPONSE MODELS

Physiologically inspired feature extraction for audio classi-fication often uses simplified parametric models of auditory processing. We employ linear and nonlinear neuron models directly derived from neural responses in zebra finches as fea-ture 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 classifica-tion 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 % ac-curacy. Nonlinear models provided up to 15 % benefit over linear models, indicating the importance of nonlinearities in classification with physiologically motivated features. Index Terms — audio classification, biological systems, physiologically motivated feature extraction 1