Investigation on Machine Learning Approaches for Environmental Noise Classifications (original) (raw)

Tis project aims to investigate the best machine learning (ML) algorithm for classifying sounds originating from the environment that were considered noise pollution in smart cities. Sound collection was carried out using necessary sound capture tools, after which ML classifcation models were utilized for sound recognition. Additionally, noise pollution monitoring using Python was conducted to provide accurate results for sixteen diferent types of noise that were collected in sixteen cities in Malaysia. Te numbers on the diagonal represent the correctly classifed noises from the test set. Using these correlation matrices, the F1 score was calculated, and a comparison was performed for all models. Te best model was found to be random forest.