Uncrewed Aircraft Detection from YAMNET Embedding Dataset

This dataset is the main dataset used by the UA Detection Team here at Embry-Riddle Aeronautical University. It provides the ability to conduct binary drone/no drone classification as well as specific drone sub-type classifcation. Dataset is gathered from field data from drones flown by the Unavail research team. Each clip of audio is fed through YAMNET to generate points in an embedding space. The no_drone directory contains various sounds from ambient noise to cars and jets. drone contains samples from the DJI Matrice M100, the Mavic 3, and the Mavic Mini 2. There are 9108 samples total across all the directories. Each .tfdata segment represents a single second of audio.

本数据集为安柏瑞德航空大学（Embry-Riddle Aeronautical University）无人机检测团队的核心实验数据集，可完成无人机与非无人机的二元分类任务，同时支持特定无人机子类型的分类工作。该数据集源自Unavail研究团队操控无人机采集的野外实测音频数据。每一段音频片段均经过YAMNET模型处理，以生成嵌入空间中的特征点。其中，no_drone文件夹涵盖了从环境噪音到汽车、喷气式飞行器的各类声音样本；drone文件夹则收录了大疆（DJI）Matrice M100、Mavic 3以及Mavic Mini 2三款无人机的音频样本。所有文件夹总计包含9108条音频样本，每个.tfdata数据片段对应时长为1秒的音频。

搜集汇总

背景与挑战

背景概述

该数据集用于无人机检测，支持二元分类（无人机/非无人机）和特定无人机子类型分类，基于实地采集的音频数据通过YAMNET生成嵌入特征，包含9108个一秒音频样本，涵盖多种无人机型号和环境噪声。