Reading: Novel Feature Extraction Algorithm for Classification of Multiple Occurrence of Flight Calls

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Novel Feature Extraction Algorithm for Classification of Multiple Occurrence of Flight Calls

Authors:

D. N. Egodage ,

University of Moratuwa, LK
About D. N.

Post Graduate Student, Department of Computer Science and Engineering

 

AMIE(SL), B.Sc. Eng. (Moratwa)

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S. J. Sooriyaarachchi

University of Moratuwa, LK
About S. J.

Lecturer, Department of Computer Science and Engineering

 

AMIE (SL), B.Sc.Eng. (Peradeniya), PhD (Moratuwa), M.Sc. (Moratuwa)

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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%.
How to Cite: Egodage, D.N. and Sooriyaarachchi, S.J., 2021. Novel Feature Extraction Algorithm for Classification of Multiple Occurrence of Flight Calls. Engineer: Journal of the Institution of Engineers, Sri Lanka, 54(2), pp.7–13. DOI: http://doi.org/10.4038/engineer.v54i2.7386
Published on 11 Aug 2021.
Peer Reviewed

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