Environmental Sound Classification for Flood Event Detection

Abstract

Flood is one of the common natural disasters that can severely affect human life and properties. Early detection, therefore, is of paramount importance to provide help through an emergency response team. Robust flood detection techniques so far have been based on computer vision using images either from cameras, satellite imagery, remote sensing, or radar-based images. However, sound signal-based flood event detection has not been widely explored. In this work, we design an end-to-end architecture for a deep learning-based flood-related sound event detection model. We employ Mel-Spectrogram-based auditory signal analysis and deep learning models for sound event detection (SED). We evaluated four deep learning models under the following two categories: (i) Binary classification Flood/No Flood, vs. Windy vs. Non-Windy, and (ii) Multi-classification for more granular flood and wind events. The experimental results performed in these settings on the datasets collected from real deployment showed an accuracy of around 78%.