SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network
By M. Cartwright, Ana Elisa Méndez Méndez, Jason Cramer, Vincent Lostanlen, G. Dove, Ho-Hsiang Wu, Justin Salamon, Oded Nov, Juan Pablo Bello. 2019
71 citations recorded by the source corpus.
SONYC Urban Sound Tagging (SONYC-UST) is a dataset for the development and evaluation of machine listening systems for real-world urban noise monitoring. It consists of 3068 audio recordings from the “Sounds of New York City” (SONYC) acoustic sensor network. Via the Zooniverse citizen science platform, volunteers tagged the presence of 23 fine-grained classes that were chosen in consultation with the New York City Department of Environmental Protection. These 23 fine-grained classes can be grouped into eight coarse-grained classes. In this work, we describe the collection of this dataset, metrics used to evaluate tagging systems, and the results of a simple baseline model
Subfields: Oceanography, Speech and Hearing, 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 Speech and Hearing
- Xavier Serra
- Eduardo Fonseca
- Xavier Favory
- Frederic Font
- Jordi Pons
- Juan Pablo Bello
- Justin Salamon
- Karol J. Piczak
In Signal Processing
- Eduardo Fonseca
- Xavier Serra
- Frederic Font
- Xavier Favory
- Jordi Pons
- Justin Salamon
- Juan Pablo Bello
- Vincent Lostanlen
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “Another case of contamination happens when a group of clips captured with the same sensor is split in training and evaluation [27].”
- Unsupervised Contrastive Learning of Sound Event Representations (2020, unknown): “We use the FSDnoisy18k dataset [21] 23, 24, 25, 21, 26]).”
- SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context (2020, methodology): “[3] for details about the taxonomy), and models are evaluated independently against the labels at each level.”
- Chirping up the Right Tree: Incorporating Biological Taxonomies into Deep Bioacoustic Classifiers (2020, background): “In contrast, hierarchical taxonomies for urban sounds requires to collect similarity judgments between acoustic events [21], which may vary across individuals, cultures, and use cases [22].”