AUDRON: A Deep Learning Framework with Fused Acoustic Signatures for Drone Type Recognition

TL;DR

AUDRON is a deep learning framework that fuses acoustic features to accurately recognize drone types, offering a low-cost, non-intrusive detection method suitable for security and surveillance contexts.

Contribution

The paper introduces AUDRON, a novel hybrid deep learning framework that combines multiple acoustic features with feature-level fusion for improved drone sound recognition.

Findings

Achieves over 98% accuracy in binary drone detection

Demonstrates high generalizability across different environmental conditions

Effectively differentiates drone sounds from background noise

Abstract

Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense. While these systems provide numerous benefits, their misuse raises safety and security concerns, making effective detection mechanisms essential. Acoustic sensing offers a low-cost and non-intrusive alternative to vision or radar-based detection, as drone propellers generate distinctive sound patterns. This study introduces AUDRON (AUdio-based Drone Recognition Network), a hybrid deep learning framework for drone sound detection, employing a combination of Mel-Frequency Cepstral Coefficients (MFCC), Short-Time Fourier Transform (STFT) spectrograms processed with convolutional neural networks (CNNs), recurrent layers for temporal modeling, and autoencoder-based representations. Feature-level fusion integrates complementary information before classification. Experimental evaluation demonstrates that AUDRON effectively differentiates drone acoustic signatures from background noise, achieving high accuracy while maintaining generalizability across varying conditions. AUDRON achieves 98.51 percent and 97.11 percent accuracy in binary and multiclass classification. The results highlight the advantage of combining multiple feature representations with deep learning for reliable acoustic drone detection, suggesting the framework's potential for deployment in security and surveillance applications where visual or radar sensing may be limited.