Cascade classifiers trained on gammatonegrams for reliably detecting audio events

In this paper we propose a novel method for the detection of events of interest through audio analysis. The system that we propose is based on the representation of the audio streams through a Gammatone image, which describes the time-frequency distribution of the energy of the signal; this representation is inspired by the functioning of the human auditory system. A pool of AdaBoost cascade classifiers, one for each class of events of interest, is involved in the event detection stage. The performance of the proposed system has been evaluated on a large data set of audio events for surveillance applications and the achieved results, compared with two state of the art approaches, confirm its effectiveness.