Generalisation in Environmental Sound Classification: The 'Making Sense of Sounds' Data Set and Challenge

Christian Kroos, Oliver Bones, Yin Cao, Lara Harris, Philip J.B. Jackson, William J. Davies, Wenwu Wang, Trevor J. Cox, Mark D. Plumbley

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

8 Citations (Scopus)

Abstract

Humans are able to identify a large number of environmental sounds and categorise them according to high-level semantic categories, e.g. urban sounds or music. They are also capable of generalising from past experience to new sounds when applying these categories. In this paper we report on the creation of a data set that is structured according to the top-level of a taxonomy derived from human judgements and the design of an associated machine learning challenge, in which strong generalisation abilities are required to be successful. We introduce a baseline classification system, a deep convolutional network, which showed strong performance with an average accuracy on the evaluation data of 80.8%. The result is discussed in the light of two alternative explanations: An unlikely accidental category bias in the sound recordings or a more plausible true acoustic grounding of the high-level categories.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8082-8086
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • Acoustic classification
  • convolutional neural network
  • deep learning
  • machine learning challenge
  • sound taxonomy

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