About this document
Machine Learning for Noise Classification by Kim Kam is a document available to read on EtoBox.
This document presents a case study on using machine learning to classify environmental noise sources and quantify the contribution of specific sources like mine noise. Preliminary results found machine learning models can identify noise sources as effectively as human listeners. When classifying long-term noise monitoring data, the machine learning models produced mine noise contribution assessments consistent with manual expert reviews. This suggests machine learning could automate noise source classifica
- Author
- Kim Kam
- Language
- EN