# SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context

arXiv: https://arxiv.org/abs/2009.05188 · HTML: https://mlsanity.com/paper/2009.05188/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2020.

## Built on by (most cited first, 12 of 12)

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- [Automatic Detection Of Noise Events at Shooting Range Using Machine Learning](https://mlsanity.com/paper/2107.11453/) (2021)
- [A Study on Robustness to Perturbations for Representations of Environmental Sound](https://mlsanity.com/paper/2203.10425/) (2022)
- [The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft](https://mlsanity.com/paper/2311.06368/) (2023)
- [UniWhisper: Efficient Continual Multi-task Training for Robust Universal Audio Representation](https://mlsanity.com/paper/2602.21772/) (2026)
- [Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering](https://mlsanity.com/paper/2206.03112/) (2022)
- [SounDiT: Geo-Contextual Soundscape-to-Landscape Generation](https://mlsanity.com/paper/2505.12734/) (2025)
- [MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification](https://mlsanity.com/paper/2506.11331/) (2025)
- [Geo-ATBench: A Benchmark for Geospatial Audio Tagging with Geospatial Semantic Context](https://mlsanity.com/paper/2603.10623/) (2026)
- [PC-Mix: Partial-Component Audio Spoofing Detection under Mixed Speech and Environmental Sound Conditions](https://mlsanity.com/paper/2607.10345/) (2026)

## Abstract (arXiv)

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.