An abnormal sound detection and classification system for surveillance applications
Cheung-Fat Chan, E.W.M. Yu · 2010
A detection and classification system for sound surveillance is presented. A human/non-human voice classifier is firstly applied to separate the input sound into human voice sound or non-human emergency sound. It utilizes a sliding window Hidden Markov Model (HMM) with trained background, human voice and non-human sound templates. In case of human voice, a scream/non-scream classification is performed to detect screaming in an abnormal situation such as screaming for help during bank robbery. In case of non-human sound, an emergency sound classifier capable of identifying abnormal sounds such as gun shot, glass breaking, and explosion, is employed. HMM is used in both scream/non-scream classification and emergency sound classification but with different sound feature sets. In this research, a number of useful sound features are developed for various classification tasks. The system is evaluated under various SNR conditions and low error rates are reported. 1.