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

Kroos, Christian and Bones, Oliver and Cao, Yin and Harris, Lara and Jackson, Philip JB and Davies, William J and Wang, Wenwu and Cox, Trevor J and Plumbley, Mark D (2019) Generalisation in Environmental Sound Classification: The ‘Making Sense of Sounds’ Data Set and Challenge. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12 May 2019 - 17 May 2019, Brighton, United Kingdom.

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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.

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