Integrating Grey Wolf Optimizer for Feature Selection in Birdsong Classification Using K-Nearest Neighbours Algorithm

Abstract

This study aims to improve the classification accuracy of birdsongs by selecting the most pertinent features. This is important because birds are exceptional environmental regulators, but many species are endangered. The community can be assisted in distinguishing bird species and conserving the local environment if the classification is more precise. Nevertheless, because of disruptive noise and unfavorable qualities in the whispering of these bird species, feature selection focuses on enhancing performance accuracy. The use of the gray wolf optimizer (GWO) technique has been employed to identify the most optimum features from the data after outlier removal by the application of k-means clustering, reducing noise through YAMNet, and feature synthesis using gammatone cepstral coefficients (GFCC). This work utilizes the GWO algorithm to address the constraint management challenges associated with high-dimensional data in birdsong classification. The fitness functions used in this research are derived from the K-nearest neighbors (KNN) algorithm. The objective is to devise innovative ways for effectively managing constraints in the context of high-dimensional data. The number of features was reduced by more than 30.7% compared to the initial number of features and obtained an accuracy of 81.06%, as determined by the evaluation outcomes. This discovery improves precision by 4% and surpasses prior research. In summary, this work showcases the effectiveness of the optimization method, especially of GWO. It makes a valuable contribution to advancing a new workflow for analyzing high-dimensional data, specifically in enhancing the classification of birdsongs.