Bayesian classification of environmental noise sources

Classification algorithms are an essential component of continuously running environmental noise monitors. Without them, one does not know which noise sources are responsible for the levels recorded by the monitor. This is problematic given that continuously recording monitors may accumulate millions of triggered events and terabytes of data. In this study, we look at the utility of Bayesian classification methods. We compare the performance of these methods to some of the top performing environmental noise classifiers (e.g., support vector machines, random forest, and bagged trees), and discuss the advantages and disadvantages of the Bayesian approach. In particular, we compare the accuracy, number of observations needed to achieve an accurate classification, computation time, and feature importance.