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Automatic Classification of Impact Sounds with Rejection of Unknown Samples

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Abstract

The discrimination of very similar sounds is a hard task both for artificial systems and humans. For the former, the main problem lies on finding appropriate features to discriminate each class of sounds, which is an especially hard task when the sounds are very similar, such as impacts on rods of different metals. This paper presents a method to automatically select the features to be used in the classification. Given an initial large set of features, the method measures their discriminative power and builds a reduced set of new features which discriminates the sound classes very accurately. This feature selection method is part of the learning phase of a supervised classification approach also proposed here. In addition, this approach contains a module that rejects unknown sounds also very accurately. This is also an important innovation since most audio classifiers assume all test sounds belong to one of the known classes.

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Notes

  1. Even though Fourier analysis gives information about both the magnitude and phase spectrum of the sound, here we consider only the magnitudes.

  2. In the remaining text, given a set \(\mathscr {X}\), we define \(\Vert \mathscr {X}\Vert\) as its size.

  3. In the remaining text (except for Sect. 5) every reasoning made for \(DP^{\prime }\) is valid for DP, unless otherwise specified or obviously not applicable.

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Acknowledgements

We would like to thank FCT MCTES and NOVA LINCS UID/CEC/04516/2013.

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Authors and Affiliations

  1. NOVA Laboratory for Computer Science and Informatics, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516, Caparica, Portugal

    Joaquim Ferreira da Silva, Sofia Cavaco & Gabriel Pereira Lopes

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  1. Joaquim Ferreira da Silva
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  2. Sofia Cavaco
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  3. Gabriel Pereira Lopes
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Correspondence to Joaquim Ferreira da Silva.

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da Silva, J.F., Cavaco, S. & Lopes, G.P. Automatic Classification of Impact Sounds with Rejection of Unknown Samples. New Gener. Comput. 35, 341–363 (2017). https://doi.org/10.1007/s00354-017-0025-z

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  • DOI: https://doi.org/10.1007/s00354-017-0025-z

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