Overview

Original Research

A MACHINE LEARNING FOR ENVIRONMENTAL NOISE MONITORING AND CLASSIFICATION USING MATLAB

ALI OTHMAN ALBAJI 1, ROZEHA BT. A. RASHID 2, MHOD ADIB BIN SARIJARI 3, ZAINAL BIN SALAM 4, SITI ZELEHA ABDUL HAMID 5, and YASEEN HADI ALI

Vol 17, No 06 ( 2022 )   |  DOI: 10.5281/zenodo.7022839   |   Author Affiliation: dept. of Electronics and Telecommunications, The Higher Institute of Science and Technology- Suk Algumaa Tripoli, Libya 1; dept. of Telecommunication Software and Systems (TeSS) Research Group, Faculty of Engineering, University Technology Malaysia, Johor Bahru, Malaysia 2; dept. of Telecommunication Software and Systems (TeSS) Research Group, Faculty of Engineering, University Technology Malaysia, Johor Bahru, Malaysia 3; dept. Electrical Energy Systems, Faculty of Engineering, University Technology Malaysia, Johor Bahru, Malaysia 4; dept. of Telecommunication Software and Systems (TeSS) Research Group, Faculty of Engineering, University Technology Malaysia, Johor Bahru, Malaysia 5; dept. of Computer Techniques Engineering, Telecommunication Software and System, Alsalam University College, Baghdad, Iraq 6.   |   Licensing: CC 4.0   |   Pg no: 194-217   |   To cite: ALI OTHMAN ALBAJI, et al., (2022). A MACHINE LEARNING FOR ENVIRONMENTAL NOISE MONITORING AND CLASSIFICATION USING MATLAB. 17(06), 194–217. https://doi.org/10.5281/zenodo.7022839   |   Published on: 13-06-2022

Abstract

This project aims to make a case study using Machine Learning (ML) classification of sounds originating from the environment which are considered noise pollution in cities and compared them with the recommended levels by international standards such as the World Health Organization (WHO). The sound collection will be carried out using necessary sound capture tools before ML classification models are utilized for sound recognition. In addition to ML, noise pollution monitoring using MATLAB will be conducted to provide accurate results of sixteen different types of noise that have been collected in Malaysia in sixteen cities. The findings are expected to provide a guideline of the conducive environment for carrying out tasks in the presence of noise and recommend measures for noise mitigation under specific conditions.

Keywords

ML, MATLAB, WHO standers, Noise monitoring, Noise pollution