Generalisation in environmental sound classification : the ‘making sense of sounds’ data set and challenge

Kroos, C, Bones, OC ORCID: https://orcid.org/0000-0002-1608-3459, Cao, Y, Harris, LE, Jackson, PJB, Davies, WJ ORCID: https://orcid.org/0000-0002-5835-7489, Wang, W, Cox, TJ ORCID: https://orcid.org/0000-0002-4075-7564 and Plumbley, MD 2019, Generalisation in environmental sound classification : the ‘making sense of sounds’ data set and challenge , in: 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019), 12-17 May 2019, Brighton, UK.

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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.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2019)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
ISBN: 9781479981311
ISSN: 2379-190X
Related URLs:
Funders: Engineering and Physical Sciences Research Council (EPSRC), European Commissions Horizon 2020
Depositing User: LE Harris
Date Deposited: 04 Mar 2019 14:27
Last Modified: 29 Nov 2019 14:30
URI: http://usir.salford.ac.uk/id/eprint/51499

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