{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/empirical-study-of-drone-sound-detection-in","title":"Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks","arxiv_id":"1701.05779","date":"2017-01-20","proceeding":null,"authors":["Sungho Jeon","Jong-Woo Shin","Young-Jun Lee","Woong-Hee Kim","YoungHyoun Kwon","Hae-Yong Yang"],"abstract":"This work aims to investigate the use of deep neural network to detect\ncommercial hobby drones in real-life environments by analyzing their sound\ndata. The purpose of work is to contribute to a system for detecting drones\nused for malicious purposes, such as for terrorism. Specifically, we present a\nmethod capable of detecting the presence of commercial hobby drones as a binary\nclassification problem based on sound event detection. We recorded the sound\nproduced by a few popular commercial hobby drones, and then augmented this data\nwith diverse environmental sound data to remedy the scarcity of drone sound\ndata in diverse environments. We investigated the effectiveness of\nstate-of-the-art event sound classification methods, i.e., a Gaussian Mixture\nModel (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network\n(RNN), for drone sound detection. Our empirical results, which were obtained\nwith a testing dataset collected on an urban street, confirmed the\neffectiveness of these models for operating in a real environment. In summary,\nour RNN models showed the best detection performance with an F-Score of 0.8009\nwith 240 ms of input audio with a short processing time, indicating their\napplicability to real-time detection systems.","url_abs":"http://arxiv.org/abs/1701.05779v1","url_pdf":"http://arxiv.org/pdf/1701.05779v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"empirical-study-of-drone-sound-detection-in","repo_url":"https://github.com/sdeva14/eusipco17-drone-sound-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sound-classification","task_name":"Sound Classification"},{"task_slug":"sound-event-detection","task_name":"Sound Event Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}