Machine learning algorithms, music genre, Decision Tree Classifier, Logistic regression, cross-validation


This article analysis a Kazakh Music dataset, which consists of 800 audio tracks equally distributed across 5 different genres. The purpose of this research is to classify music genres by using machine learning algorithms Decision Tree Classifier and Logistic regression. Before the classification, the given data was pre-processed, missing or irrelevant data was removed. The given dataset was analyzed using a correlation matrix and data visualization to identify patterns. To reduce the dimension of the original dataset, the PCA method was used while maintaining variance. Several key studies aimed at analyzing and developing machine learning models applied to the classification of musical genres are reviewed.

Cumulative explained variance was also plotted, which showed the maximum proportion (90%) of discrete values ​​generated from multiple individual samples taken along the Gaussian curve. A comparison of the decision tree model to a logistic regression showed that for f1 Score Logistic regression produced the best result for classical music - 82%, Decision tree classification - 75%. For other genres, the harmonic mean between precision and recall for the logistic regression model is equal to zero, which means that this model completely fails to classify the genres Zazz, Kazakh Rock, Kazakh hip hop, Kazakh pop music. Using the Decision tree classifier algorithm, the Zazz and Kazakh pop music genres were not recognized, but Kazakh Rock with an accuracy and completeness of 33%. Overall, the proposed model achieves an accuracy of 60% for the Decision Tree Classifier and 70% for the Logistic regression model on the training and validation sets. For uniform classification, the data were balanced and assessed using the cross-validation method.

The approach used in this study may be useful in classifying different music genres based on audio data without relying on human listening.


Patil, S.A., Pradeepini, G., & Komati, T.R. (2023). Novel mathematical model for the classification of music and rhythmic genre using deep neural network. J Big Data, 10, 108.

Liu, C., Feng, L., Liu, G., & Shenglan, L. (2021). Bottom-up broadcast neural network for music genre classification. Multimedia Tools Application, 80, 7313-7331. 10.1007/s11042-020-09643-6

Ghildiyal, A., Singh, K., & Sharma, S. (2020). Music genre classification using machine learning. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 1368–1372.

Cheng, Y., Chang, P., Nguyen, D., & Che-Nan, K. (2021). Automatic music genre classification based on CRNN. Eng Lett., 29(1), 36-48.

Foleis, J.H., & Tavares, T.F. (2020). Texture selection for automatic music genre classification. Appl Soft Comput., 89, 106127.

Elbir, A., & Aydin, N. (2020). Music genre classification and music recommendation by using deep learning. Electron Lett., 56(12), 627–9.

Ramírez, J., & Flores, M.J. (2020). Machine learning for music genre: multifaceted review and experimentation with audio set. J Intell INF Syst., 55, 469–499.

Mounika, K., Deyaradevi, S., Swetha, K., & Vanitha, V. (2021). Music Genre Classification Using Deep Learning. 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 1-7.

Pelchat, N., & Gelowitz, C.M. (2020). Neural network music genre classification. Can J Electr Comput Eng., 43(3), 170–173.

Suero, M., Gassen, C.P., Mitic, D., Xiong, N., & Leon, M. (2020). A Deep Neural Network Model for Music Genre Recognition. Advances in Intelligent Systems and Computing, 1074.

Liang, B., & Gu, M. (2020). Music genre classification using transfer learning. 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), 392-393.

Ghosal, S., & Sarkar, I. (2020). Novel approach to music genre classification using clustering augmented learning method (CALM). AAAI Spring Symposium Combining Machine Learning with Knowledge Engineering, 29 (1).

Parmezan, A., Silva, D., & Batista, G. (2020). A combination of local approaches for hierarchical music genre classification. Proceedings of the 21st ISMIR conference, Montreal, Canada, October 11–16.

Yuan, C., Ma, Q., Chen G., Jhou W., Zhang X., Tang X., Han J., & Hu S. (2020). Exploiting heterogeneous artist and listener preference graph for music genre classification. Poster Session A3: Multimedia search and recommendation & multimedia system and middleware.

Zhuang, Y., Chen, Y., & Zheng, J. (2020). Music genre classification with transformer classifier. Proceedings of the 2020 4th international conference on digital signal processing, 155-159.

De Araújo, L.R., de Sousa, R., & Barbosa, S.D. (2020). Brazilian lyrics-based music genre classification using a BLSTM network. Lecture notes in computer science, 415, 522-534.

Budhrani, A., Patel, A., & Ribadiya, S. (2021). Music2Vec: Music Genre Classification and Recommendation System. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 1406-1411 ICECA49313.2020.9297559

Chang, S., Abdul A., Chen J., &, Liao, H. (2018). A personalized music recommendation system using convolutional neural networks approach. 2018 IEEE International Conference on Applied System Invention (ICASI), 47-49.

Hyung, Z., Park, J.S., & Lee, K. Utilizing context-relevant keywords extracted from a large collection of user-generated documents for music discovery. Info. Processing and Management, 53 (5), 1185-1200.

Jandaghian, M., Setayeshi, S., Razzazi, F., & Sharifi, A. (2023). Music emotion recognition based on a modified brain emotional learning model. Multimed Tools Appl., 82, 26037–26061




How to Cite

Mimenbayeva, A., Bekmagambetova, G., Muratova, G. ., Naizagarayeva, A. ., Ospanova , T. ., & Konyrkhanova , A. (2024). CLASSIFICATION OF KAZAKH MUSIC GENRES USING MACHINE LEARNING TECHNIQUES. Scientific Journal of Astana IT University, 17(17), 83–94.



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