Identifying noise levels of individual rail pass by events

Abstract

Technology associated with acoustic data capture has advanced significantly, with commercially available Sound Level Meters allowing engineers and consultants to capture large amounts of multi-channel data relating to train noise. Whilst this extended dataset can provide vital information, manually scrutinizing large amounts of data to isolate individual train pass-bys can be time consuming and problematic. This paper investigates the implementation of automated, remote (un-manned) systems that can be installed on-site, allowing train pass-by noise levels to be recorded with minimal user guidance. The efficacy of acoustic and ground vibration sensors to accurately identify train noise levels and train direction is investigated.