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/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 publicationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages8082-8086
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
ISBN (Print)978-1-4799-8132-8
DOIs
Publication statusE-pub ahead of print - 17 Apr 2019
Event44th International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
https://www.2019.ieeeicassp.org/

Publication series

Name
PublisherIEEE
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference44th International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19
Internet address

Keywords

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

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