Deep learning for unsupervised separation of environmental noise sources
Bryan Wilkinson, Charlotte L. Ellison, Edward T. Nykaza, Arnold P. Boedihardjo, Anton D. Netchaev, Zhiguang Wang, Steven L. Bunkley, Tim Oates, Matthew G. Blevins · The Journal of the Acoustical Society of America · 2017
With the advent of reliable and continuously operating noise monitoring systems, we are now faced with an unprecedented amount of noise monitor data. In the context of environmental noise monitoring, there is a need to automatically detect, separate, and classify all environmental noise sources. This is a complex task because sources can overlap, vary by location, and have an unbounded number of noise sources that a monitor device may record. In this study, we synthetically generate datasets that contain Gaussian noise and overlaps for several pre-labeled environmental noise monitoring datasets to examine how well deep learning methods (e.g., autoencoders) can separate environmental noise sources. In addition to examining performance, we also focus on understanding which signal features and separation metrics are useful to this problem.