Cascade classifiers for audio classification

We present a set of features derived from a model of the early auditory system and the primary auditory cortex. We show that a classification scheme based on AdaBoost works better than a GMM-based method, especially when the feature dimensions are large. Different variations of the AdaBoost-based approach are compared, and it is shown that a cascade of classifiers approach gives high accuracy while reducing the computation time and power by allocating resources proportional to the classification difficulty of the example being considered. For all classifiers considered, both training and testing are performed on one second segments.