Sky-Ear: An Unmanned Aerial Vehicle-Enabled Victim Sound Detection and Localization System

TL;DR

Sky-Ear is an UAV-based system that uses a novel two-stage audio processing approach with a Masking autoencoder for efficient victim sound detection and precise localization in search-and-rescue missions.

Contribution

The paper introduces a new UAV-enabled victim sound detection system with a two-stage process and a Masking autoencoder for improved accuracy and energy efficiency.

Findings

Validated system performance through extensive simulations.

Achieved high victim detection accuracy.

Reduced localization error with optimized methods.

Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in search-and-rescue (SAR) missions, yet continuous and reliable victim detection and localization remain challenging due to on-board hardware constraints. This paper designs an UAV-Enabled Victim Sound Detection and Localization System (called ``Sky-Ear'' for brevity) to achieve energy-efficient acoustic sensing and sound detection for SAR. Based on a circular-shaped microphone array, two-stage (Sentinel and Responder) audio processing is developed for energy-consuming and highly reliable sound detection. A Masking autoencoder (MAE)-based sound detection method is designed in the Sentinel stage to analyze frequency-time acoustic features. For improved precision, a continuous localization method is designed by optimizing detected directions from multiple observations. Extensive simulation experiments are conducted to validate the system's performance in terms of victim detection accuracy and localization error.