Multiple classifiers fusion to classify acoustic events in ONC hydrophone data

In this paper, we present a new framework of multiple classifiers fusion to classify acoustic events in ONC (Ocean Network Canada) hydrophone data. The outputs of three different classifiers are fused based on aggregation of a generated decision matrix. An ensemble class label is thereby obtained for the classification of acoustic events into multiple classes of whale calls, boat sounds and noise. The classification performances are evaluated using real recorded hydrophone data showing an overall improvement of the classification accuracy by 10% for the proposed method over the average accuracy of the individual classifiers.