GRAPH-BASED MULTI-SENSOR FUSION FOR ACOUSTIC SIGNAL CLASSIFICATION

Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e. rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as recently proposed joint sparsity models for multi-sensor acoustic signal classification.