论文标题
sonyc-ust-v2：带有时空上下文的城市声音标记数据集
SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
论文作者
论文摘要
我们提出了Sonyc-ust-V2，这是一个使用时空信息的城市声音标签的数据集。该数据集旨在开发和评估机器听力系统，以进行现实世界中的城市噪声监测。尽管有城市记录的数据集可用，但该数据集提供了一个机会，可以调查时空元数据如何帮助预测城市声音标签。 Sonyc-ust-V2由“纽约市的声音”（Sonyc）声传感器网络中的18510年录音组成，包括音频获取的时间戳和传感器的位置。该数据集包含了Zooniverse Citizen Science平台的志愿者的注释，以及与我们的团队进行的两阶段验证。在本文中，我们描述了我们的数据收集程序，并提出了用于城市声音标签多标签分类的评估指标。我们报告了利用时空信息的简单基线模型的结果。
We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. The dataset contains annotations by volunteers from the Zooniverse citizen science platform, as well as a two-stage verification with our team. In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags. We report the results of a simple baseline model that exploits spatiotemporal information.
