Abrupt change detection in railway noise data

Current methods for diagnosing the quality of the railway superstructure are mainly based on optical sensors, which are relatively expensive compared to acoustic sensors. As part of the HLUKOS research project, a pair of microphones is installed near the wheel-rail contact point on a diagnostic vehicle of the Railway Administration (Czech railway infrastructure manager). The research task is to detect when the sound level changes significantly. A likelihood ratio method has been used in this paper to detect abrupt changes, which is a current scientific topic. Experiments with different input thresholds are performed on a sample of 250 m of track data. Initial experimental results show that this method is meaningfully able to detect locations of abrupt changes with input threshold values h = 4.58 and number of steps from N = 5 to N = 40.