Comparison of some Feature Extraction and Classification Methods for Bee Sound Data

Date Received: 25-11-2024

Date Accepted: 31-07-2025

Date Published: 31-07-2025

##submissions.doi##: https://doi.org/10.31817/tckhnnvn.2025.23.7.06

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Huyen, N., & Hoang, N. (2025). Comparison of some Feature Extraction and Classification Methods for Bee Sound Data. Vietnam Journal of Agricultural Sciences, 23(7), 899–909. https://doi.org/10.31817/tckhnnvn.2025.23.7.06

Comparison of some Feature Extraction and Classification Methods for Bee Sound Data

Thi Huyen Nguyen (*) 1 , Nguyen Van Hoang 1

  • Tác giả liên hệ: [email protected]
  • 1 Khoa Công nghệ thông tin, Học viện Nông nghiệp Việt Nam

    • Keywords

    MFCC, Chroma, Wavelet, Random Forest, XGBoost, Beehive sound recognition

    Abstract


    In this study, we have tested and evaluated several feature extraction methods of bee sound data. The features were extracted by MFCC, Chroma and Wavelet techniques and important features were selected by Random Forest (RF), Extra trees, and XGBoost method from raw data and then provided to machine learning algorithms such as SVM, Random Forest, XGBoost to solve the bee recognition problem. The experiment results show that using Random Forest and XGBoost models with MFCC features, bee sound recognition is fully possible with the accuracy over 99.9%.

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