  In this work, we present a general procedure for acoustic leak detection in
water networks that satisfies multiple real-world constraints such as energy
efficiency and ease of deployment. Based on recordings from seven contact
microphones attached to the water supply network of a municipal suburb, we
trained several shallow and deep anomaly detection models. Inspired by how
human experts detect leaks using electronic sounding-sticks, we use these
models to repeatedly listen for leaks over a predefined decision horizon. This
way we avoid constant monitoring of the system. While we found the detection of
leaks in close proximity to be a trivial task for almost all models, neural
network based approaches achieve better results at the detection of distant
leaks.
