A dataset and taxonomy for urban sound research

Justin Salamon, Christopher Jacoby, Juan Pablo Bello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Automatic urban sound classification is a growing area of research with applications in multimedia retrieval and urban informatics. In this paper we identify two main barriers to research in this area - the lack of a common taxonomy and the scarceness of large, real-world, annotated data. To address these issues we present a taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes. The challenges presented by the new dataset are studied through a series of experiments using a baseline classification system.

Original languageEnglish (US)
Title of host publicationMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages1041-1044
Number of pages4
ISBN (Electronic)9781450330633
DOIs
StatePublished - Nov 3 2014
Event2014 ACM Conference on Multimedia, MM 2014 - Orlando, United States
Duration: Nov 3 2014Nov 7 2014

Publication series

NameMM 2014 - Proceedings of the 2014 ACM Conference on Multimedia

Other

Other2014 ACM Conference on Multimedia, MM 2014
CountryUnited States
CityOrlando
Period11/3/1411/7/14

Keywords

  • Classification
  • Dataset
  • Taxonomy
  • Urban sound

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Media Technology
  • Software

Fingerprint Dive into the research topics of 'A dataset and taxonomy for urban sound research'. Together they form a unique fingerprint.

  • Cite this

    Salamon, J., Jacoby, C., & Bello, J. P. (2014). A dataset and taxonomy for urban sound research. In MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia (pp. 1041-1044). (MM 2014 - Proceedings of the 2014 ACM Conference on Multimedia). Association for Computing Machinery, Inc. https://doi.org/10.1145/2647868.2655045