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Article

Music Genre Classification Based on VMD-IWOA-XGBOOST

1
School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243002, China
2
Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma’anshan 243032, China
3
School of Management Science and Engineering, Anhui University of Technology, Ma’anshan 243002, China
4
School of Management Science and Real Estate, Chongqing University, Chongqing 400045, China
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(10), 1549; https://doi.org/10.3390/math12101549
Submission received: 17 April 2024 / Revised: 29 April 2024 / Accepted: 13 May 2024 / Published: 15 May 2024

Abstract

Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria.
Keywords: music genre classification; feature extraction; decomposition; optimization music genre classification; feature extraction; decomposition; optimization

Share and Cite

MDPI and ACS Style

Gan, R.; Huang, T.; Shao, J.; Wang, F. Music Genre Classification Based on VMD-IWOA-XGBOOST. Mathematics 2024, 12, 1549. https://doi.org/10.3390/math12101549

AMA Style

Gan R, Huang T, Shao J, Wang F. Music Genre Classification Based on VMD-IWOA-XGBOOST. Mathematics. 2024; 12(10):1549. https://doi.org/10.3390/math12101549

Chicago/Turabian Style

Gan, Rumeijiang, Tichen Huang, Jin Shao, and Fuyu Wang. 2024. "Music Genre Classification Based on VMD-IWOA-XGBOOST" Mathematics 12, no. 10: 1549. https://doi.org/10.3390/math12101549

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