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Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples

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Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10198))

Abstract

Instrument recognition in polyphonic audio recordings is a very complex task. Most research studies until now were focussed on the recognition of Western instruments in Western classical and popular music, but also an increasing number of recent works addressed the classification of ethnic/world recordings. However, such studies are typically restricted to one kind of music and do not measure the bias of “Western” effect, i.e., the danger of overfitting towards Western music when the classification models are optimised only for such tracks. In this paper, we analyse the performance of several instrument classification models which are trained and optimised on polyphonic mixtures of Western instruments, but independently validated on mixtures created with randomly added ethnic samples. The conducted experiments include evolutionary multi-objective feature selection from a large set of audio signal descriptors and the estimation of individual feature relevance.

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Notes

  1. 1.

    http://compmusic.upf.edu/publications, accessed on 15.11.2016.

  2. 2.

    http://theremin.music.uiowa.edu/MIS.html, accessed on 15.11.2016.

  3. 3.

    http://www.bestservice.de/en/ethno_world_5_professional__voices.html, accessed on 15.11.2016.

  4. 4.

    In this study, the reference point is (1,1): a theoretical solution which uses all features and leads to the classification error \(e=1\).

  5. 5.

    For all applied tests in this paper, we use a standard value of 5% for the significance level.

  6. 6.

    The statistical observations are shortened for simplicity reasons and should be interpreted with certain restrictions. Obviously, they hold only for tested instruments, mixtures, features, feature processing, and feature selection method.

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Authors and Affiliations

  1. Department of Computer Science, TU Dortmund, Dortmund, Germany

    Igor Vatolkin

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  1. Igor Vatolkin
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Correspondence to Igor Vatolkin .

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  1. University of Coimbra , Coimbra, Portugal

    João Correia

  2. RMIT University , Melbourne, Victoria, Australia

    Vic Ciesielski

  3. University of Malta , Msida, Malta

    Antonios Liapis

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Vatolkin, I. (2017). Generalisation Performance of Western Instrument Recognition Models in Polyphonic Mixtures with Ethnic Samples. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-55750-2_21

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