MAESTRO Synthetic - Multi-Annotator Estimated Strong Labels

The dataset was created for studying estimation of strong labels using crowdsourcing. It contains 20 synthetic audio files created using Scaper, the reference annotation created with Scaper, and the annotation outcome. Annotation was performed using Amazon Mechanical Turk. Audio files contain excerpts of recordings uploaded to freesound.org.(from Urban Sound 8k dataset). Please see FREESOUNDCREDITS.txt for an attribution list. The dataset contains: audio: the 20 synthetic soundscapes, each 3 min long ground truth: the "true" reference annotation created using Scaper estimated strong labels: the reference annotation created from the crowdsourced data audio tags: the weak labels corresponding to each 10 s segment of the soundscapes, as annotated For details on the annotation procedure and label processing methodology, see the following paper: Irene Martin Morato, Manu Harju, and Annamaria Mesaros. <em>Crowdsourcing strong labels for sound event detection.</em> In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2021). New Paltz, NY, Oct 2021.本数据集专为研究基于众包的强标签（strong labels）估计任务而构建，包含20条通过Scaper生成的合成音频文件、基于Scaper制作的标准参考标注结果，以及标注产出结果。本次标注依托Amazon Mechanical Turk平台开展。音频文件均取自上传至freesound.org的录音片段（源自Urban Sound 8K数据集）。有关署名详情，请查阅FREESOUNDCREDITS.txt文件。 本数据集包含以下内容： - 音频：20条合成音景，每条时长3分钟 - 基准真值（Ground Truth）：通过Scaper生成的‘真实’标准参考标注 - 估计强标签：基于众包数据生成的参考标注 - 音频标签：对应每条音景每10秒分段的弱标签（weak labels），由标注得到 有关标注流程与标签处理方法的详细说明，请参阅以下论文：Irene Martín Morato、Manu Harju与Annamaria Mesaros。《用于声音事件检测的众包强标签构建》，发表于2021年IEEE音频与声学信号处理应用研讨会（WASPAA 2021），美国纽约州新帕尔茨，2021年10月。