Self-Supervised Drone Detection Using Acoustic Data

In recent years, the research focus on drone detection and identification via acoustic signals has grown significantly, spanning applications in both commercial and military domains. The predominant approach in this research area involves utilizing labeled data in the supervised machine learning models. However, this approach has inherent limitations as it trains models to detect or identify specific drone types present in the training dataset, constraining their generalization capacity to new drone types and tasks. This paper introduces a novel solution by employing selfsupervised learning to address these limitations. The proposed model, trained without the need for labeled data, exhibits the capability to classify and identify diverse drone types and objects. It learns to differentiate between different drone models by training with a specific drone model, creating representations suitable for various downstream tasks such as classification and identification. Experimental results demonstrate the superiority of this selfsupervised method over state-of-the-art supervised approaches, particularly in the context of drone detection benchmarks that lack label information.