In this paper, we describe an Acoustic Event Recognition (AER) system for locating events of interest in the audio stream of multimedia recordings. We focus on two non-speech acoustic events; bomb explosions and gunfire, which typically exist in surveillance videos and are of importance in monitoring and alerting applications. Recognition is performed using a discriminative approach based on Support Vector Machines (SVM). We compare the new approach to a baseline system that utilizes a Hidden Markov Model (HMM)-based classification approach. We performed experiments on a corpus of publicly available video files containing gunfire and explosion events. Our results show that the new discriminative approach, when configured to use a rich combination of acoustic features, achieves a high retrieval precision at a notable recall under noisy conditions. As compared to HMM-based system, we achieved 54% relative improvement in F-score for explosion recognition with 1.5% relative improvement in F-score for gunfire recognition.
['Saurabh Khanwalkar', 'Guruprasad Saikumar', 'Amit Srivastava', 'Premkumar Natarajan']
Discriminative Acoustic Event Recognition in multimedia recordings