🤖 AI Summary
Existing UAV detection systems exhibit limited performance in challenging environments—such as low-light conditions and long-range scenarios—where visual and RF-based methods often fail.
Method: This work proposes an acoustic-feature-based multi-class UAV identification framework. We introduce the first publicly available, high-quality acoustic dataset comprising 32 mainstream UAV models, including raw audio recordings, time-frequency spectrograms, and MFCC feature maps, all captured under realistic multi-distance and multi-angle conditions. Concurrently, we develop a lightweight, interactive web platform enabling real-time audio playback, spectrogram visualization, and feature exploration.
Contribution/Results: The dataset significantly enhances discriminability of acoustic signatures for UAV classification. Empirical evaluation demonstrates strong generalization capability across diverse UAV models and operating conditions. The platform has received positive feedback from researchers and educators. This work provides foundational data resources and open-source tools to advance UAV acoustic sensing, edge-based detection, and AI-enabled security governance.
📝 Abstract
The rapid proliferation of drones across various industries has introduced significant challenges related to privacy, security, and noise pollution. Current drone detection systems, primarily based on visual and radar technologies, face limitations under certain conditions, highlighting the need for effective acoustic-based detection methods. This paper presents a unique and comprehensive dataset of drone acoustic signatures, encompassing 32 different categories differentiated by brand and model. The dataset includes raw audio recordings, spectrogram plots, and Mel-frequency cepstral coefficient (MFCC) plots for each drone. Additionally, we introduce an interactive web application that allows users to explore this dataset by selecting specific drone categories, listening to the associated audio, and viewing the corresponding spectrogram and MFCC plots. This tool aims to facilitate research in drone detection, classification, and acoustic analysis, supporting both technological advancements and educational initiatives. The paper details the dataset creation process, the design and implementation of the web application, and provides experimental results and user feedback. Finally, we discuss potential applications and future work to expand and enhance the project.