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Data-driven cymbal bronze alloy identification via evolutionary machine learning with automatic feature selection

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Abstract

This paper aims to implement four machine learning models using Differential Evolution to tune internal parameters and for feature selection in a problem involving the classification of drum cymbals according to their bronze alloys via their sound. In order to conduct the experiments, 276 audios referring to 4 cymbals were captured at a recording studio with a controlled environment and conditions. Then, 18 temporal attributes were extracted from each audio file, aiming to retrieve information from them. The experimental results show that the Extreme Gradient Boosting model combined with Differential Evolution for parameter tuning showed consistent results in all performance metrics. Furthermore, when this evolutionary algorithm selects the attributes, a considerable increase in performance is obtained, reaching 98.90% average accuracy.

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Acknowledgements

The authors acknowledge the ARGUS studio for its support in the data acquisition procedure. The authors thank the anonymous reviewers for their valuable contributions to this paper.

Funding

The authors acknowledge the Brazilian funding agencies CNPq (429639/2016-3 and 304329/2019-3), FAPEMIG (APQ-00334/18 and PPM-00001-18), and CAPES - Finance Code 001 for their financial support.

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Authors and Affiliations

  1. Computational Modeling Program, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil

    Tales H. A. Boratto

  2. State University of Minas Gerais, Divinópolis, MG, Brazil

    Camila M. Saporetti

  3. Department of Computing and Mechanics, Federal Center for Technological Education of Minas Gerais, Leopoldina, MG, Brazil

    Samuel C. A. Basilio

  4. Department of Computational and Applied Mechanics, Federal University of Juiz de Fora, Juiz de Fora, MG, Brazil

    Alexandre A. Cury & Leonardo Goliatt

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  1. Tales H. A. Boratto
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Correspondence to Leonardo Goliatt.

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Source codes are made available by the authors upon request. The audio database is free and available on the Mendeley Data Repository(https://data.mendeley.com/datasets/9tytvdxd24/1) [7].

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Boratto, T.H.A., Saporetti, C.M., Basilio, S.C.A. et al. Data-driven cymbal bronze alloy identification via evolutionary machine learning with automatic feature selection. J Intell Manuf 35, 257–273 (2024). https://doi.org/10.1007/s10845-022-02047-3

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