No abstract

Acoustic monitoring of migratory birds is becoming a demand with respect to public policy related to wind power because wind mills are responsible for the death of a large number of migratory birds. Acoustic monitoring is associated with three main processes, namely pre-processing, feature extraction and classification. An improved algorithm that can extract features has been developed in this research by combining well known MSER technique with traditional techniques. Extracted features from the said algorithm and other algorithms were combined to create three different feature sets. Classification techniques, including kNN, RF, SVM and DNN, were used to evaluate a realworld dataset in terms of the extracted features. The feature extraction technique proposed in this research. namely SMSER, performs better than SATF feature set alone and combination of SATF and SIFS feature sets with the highest performing classifier DNN with an accuracy of 87.67%.