Time-frequency analysis for audio event detection in real scenarios

We propose a sound analysis system for the detection of audio events in surveillance applications. The method that we propose combines short- and long-time analysis in order to increase the reliability of the detection. The basic idea is that a sound is composed of small, atomic audio units and some of them are distinctive of a particular class of sounds. Similarly to the words in a text, we count the occurrence of audio units for the construction of a feature vector that describes a given time interval. A classifier is then used to learn which audio units are distinctive for the different classes of sound. We compare the performance of different sets of short-time features by carrying out experiments on the MIVIA audio event data set. We study the performance and the stability of the proposed system when it is employed in live scenarios, so as to characterize its expected behavior when used in real applications.