Classification of Urban Sounds with PSO and WO Based Feature Selection Methods

Abstract

The increase in the rate of urbanization in recent years has led to an increase in environmental sound sources and, accordingly, an increase in noise pollution. Street noises, especially in big cities, pose some health problems. In terms of smart cities, accurate detection of street sounds is important in detecting unwanted sounds and responding to emergencies. In this study, research was carried out to select acoustic features of street sounds with meta-heuristic methods. In the experimental study, using the Urbansound8k dataset, feature extraction was done through openSMILE software, then feature selection was performed with PSO and WO algorithms. SVM and k-NN methods were applied for the classification process. Accuracy rates were obtained with SVM and k-NN classifiers as 88.12%, 69.32% in the PSO algorithm, 88.39%, and 70.51% in the WO algorithm, respectively.