Feature selection for acoustic events detection

Abstract

The paper deals with the detection of abnormal situations via captured sound processing. Different settings of feature extraction algorithms were realized and evaluated. Chosen feature sets were used for building the effective parametric representation for gun shots and breaking glass. This way two types of high dimensional feature supervectors were created in regard to the best individual settings of each feature extraction algorithm. For improving the recognition rate Minimum Redundancy Maximum Relevance (MRMR) and Joint Mutual Information (JMI) feature selection algorithms were also applied. They were used for the selection of superior features and for the creation of n-dimensional feature supervectors. The investigation of the appropriate dimension of feature supervectors was performed too. The framework for recognition of potentially dangerous acoustic events such as breaking glass and gun shots, based on the MRMR and JMI selected feature supervector through Hidden Markov Models based classification is proposed in the paper.