Investigation on Machine Learning Approaches for Environmental Noise Classifications

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