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

By M. Cartwright, Jason Cramer, Ana Elisa Méndez Méndez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, G. Dove, C. Mydlarz, Justin Salamon and 2 more. 2020

49 citations recorded by the source corpus.

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.

Subfields: Developmental Biology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Developmental Biology

1. Xavier Serra
2. Jordi Pons
3. Eduardo Fonseca
4. Xavier Favory
5. Frederic Font

In Signal Processing

1. Eduardo Fonseca
2. Xavier Serra
3. Frederic Font
4. Xavier Favory
5. Jordi Pons
6. Andrés Ferraro
7. Jakob Abeßer
8. D. Ellis

Citations in the corpus

• FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “Other datasets are domain-specific, with a vocabulary focused on a specific scope, such as SONYC-UST-V2 for urban sounds [34].”