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

Kroos, Christian, Bones, Oliver, Cao, Yin, Harris, Lara, Jackson, Philip J. B., Davies, William J., Wang, Wenwu, Cox, Trevor J. and Plumbley, Mark D. (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 (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Kroos, Christianc.kroos@surrey.ac.uk
Bones, Oliver
Cao, Yinyin.cao@surrey.ac.uk
Harris, Lara
Jackson, Philip J. B.P.Jackson@surrey.ac.uk
Davies, William J.
Wang, WenwuW.Wang@surrey.ac.uk
Cox, Trevor J.
Plumbley, Mark D.m.plumbley@surrey.ac.uk
Date : 12 May 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC), European Commissions Horizon 2020
Grant Title : Making Sense of Sounds
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Acoustic classification; Machine learning challenge; Sound taxonomy; Deep learning; Convolutional neural network
Related URLs :
Depositing User : Clive Harris
Date Deposited : 04 Mar 2019 14:27
Last Modified : 10 Apr 2019 12:07
URI: http://epubs.surrey.ac.uk/id/eprint/850658

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