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_posts/research/2021-03-03-Water_Networks.md
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---
layout: single
title: "Acoustic Leak Detection"
categories: research audio deep-learning anomalie-detection
excerpt: "Anomalie based Leak Detection in Water Networks"
header:
teaser: assets/figures/10_water_networks_teaser.jpg
---
![Approach](\assets\figures\10_water_networks_approach.jpg){:style="display:block; width:40%" .align-right}
This study introduces a method for acoustic leak detection in water networks, focusing on energy efficiency and easy deployment. Utilizing recordings from microphones on a municipal water network, various anomaly detection models, both shallow and deep, were trained. The approach mimics human leak detection methods, allowing intermittent monitoring instead of constant surveillance. While detecting nearby leaks proved easy for most models, neural network-based methods excelled at identifying leaks from a distance, showcasing their effectiveness in practical applications.
{% cite muller2021acoustic %}
![Leak-Mels](\assets\figures\10_water_networks_mel.jpg){:style="display:block; width:85%" .align-center}