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Machine Learning-Based Music Genre Classification with Pre-Processed Feature Analysis

Md Shofiqul Islam, Md Munirul Hasan, Md Abdur Rahim, Ali Muttaleb Hasan, Mohammad Mynuddin, Imran Khandokar, Md Jabbarul Islam


The growth of the entertainment industry around the world may be seen in the creation of new genres and the influx of artists and musicians into this field. Every day, a large amount of music is generated and released. The classification of these music based on genres and the recommendation of music to users is a crucial task for various music streaming platforms. Many artificial intelligence methods have been created to overcome this. Inadequate data for training is one of the biggest issues when it comes to building machine learning algorithm. A certain dataset contains a large number of redundant features, which may lead the models to overfit. Data filtering could be used to solve this issue. On the GTZAN data for music genre classification, this article constructed numerous Artificial Intelligence (AI) models and used a data filtering strategy. This study does a comparative analysis and discusses the results. The models developed and evaluated are Naive Bayes, Stochastic Gradient Descent, KNN, Decision trees, Random Forest, Support Vector Machine, Logistic Regression, Neural Nets, Cross Gradient Booster, Cross Gradient Booster (Random Forest) and XGBoost.  Accuracy gained by Naive Bayes is 51.95%, Stochastic Gradient Descent 65.53%, KNN 80.58%, Decision trees  63.997%, Random Forest is 81.41% , Support Vector Machine 75.41%, Logistic Regression 69.77%, Neural Nets 67.73%, Cross Gradient Booster 90.22%, Cross Gradient Booster (Random Forest) 74.87%.Finally, XGBoost is the best performed machine learning with accuracy of 90.22%. This research outcomes will help to analyse music in different areas.


Machine learning, Music, Speech, Classification, Artificial Intelligence, Filtering, Pre-processing.

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M. McKinney and J. Breebaart, "Features for audio and music classification," 2003.

M. S. I. Shofiqul, N. Ab Ghani, and M. M. Ahmed, "A review on recent advances in Deep learning for Sentiment Analysis: Performances, Challenges and Limitations," COMPUSOFT: An International Journal of Advanced Computer Technology, vol. 9, no. 7, pp. 3768-3776, 2020.

I. Khandokar, M. Hasan, F. Ernawan, S. Islam, and M. Kabir, "Handwritten character recognition using convolutional neural network," in Journal of Physics: Conference Series, 2021, vol. 1918, no. 4: IOP Publishing, p. 042152.

Purwono, A. Ma'arif and A. Wulandari, "Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering," 2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2021, pp. 347-353.

M. S. Islam, S. Sultana, U. kumar Roy, and J. Al Mahmud, "A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work," Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 6, no. 2, pp. 47-57, 2020.

M. S. Islam, S. Sultana, and M. J. Islam, "New Hybrid Deep Learning Method to Recognize Human Action from Video," Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 7, no. 2, pp. 306-313, 2021.

L. Fan, Z. Yin, H. Yu, and A. Gilliland, "Using Data-driven Analytics to Enhance Archival Processing of the COVID-19 Hate Speech Twitter Archive (CHSTA)," PrePrint, 2020.

M. M. Hasan, M. S. Islam, S. A. Bakar, M. M. Rahman, and M. N. Kabir, "Applications of Artificial Neural Networks in Engine Cooling System," in 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), IEEE Computer Society, pp. 471-476, 2021.

M. S. Islam, S. Sultana, U. K. Roy, J. Al Mahmud, and S. Jahidul, "HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text," Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 7, no. 1, pp. 142-153, 2021.

M. S. Islam and N. A. Ghani, "A Novel BiGRUBiLSTM Model for Multilevel Sentiment Analysis Using Deep Neural Network with BiGRU-BiLSTM," Singapore, 2022: Springer Singapore, pp. 403-414.

M. A. Rahim, M. Rahman, M. A. Rahman, A. J. M. Muzahid, and S. F. Kamarulzaman, "A Framework of IoT-Enabled Vehicular Noise Intensity Monitoring System for Smart City," Advances in Robotics, Automation and Data Analytics: Selected Papers from ICITES 2020, vol. 1350, p. 194, 2021.

L. C. Kiew, A. J. M. Muzahid, and S. F. Kamarulzaman, "Vehicle Route Tracking System based on Vehicle Registration Number Recognition using Template Matching Algorithm," in 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM), pp. 249-254.

Y. Kim, "Convolutional neural networks for sentence classification," MS thesis. University of Waterloo, 2014.

J. Xu, D. Chen, X. Qiu, and X. Huang, "Cached long short-term memory neural networks for document-level sentiment classification," arXiv preprint arXiv:1610.04989, 2016.

S. Lai, L. Xu, K. Liu, and J. Zhao, "Recurrent convolutional neural networks for text classification," in Twenty-ninth AAAI conference on artificial intelligence, 2015.

L. LI, A. ZHOU, Y. LIU, S. QIAN, and H. GENG, "Aspect-based sentiment analysis based on dynamic attention GRU," Scientia Sinica Informationis, vol. 49, no. 8, pp. 1019-1030, 2019.

D. Bisharad and R. H. Laskar, "Music Genre Recognition Using Residual Neural Networks," in TENCON 2019-2019 IEEE Region 10 Conference (TENCON), IEEE, pp. 2063-2068, 2019.

H. Yang and W.-Q. Zhang, "Music Genre Classification Using Duplicated Convolutional Layers in Neural Networks," in INTERSPEECH, 2019, pp. 3382-3386.

H. Bahuleyan, "Music genre classification using machine learning techniques," arXiv preprint arXiv:1804.01149, 2018.

S. Das, "Filters, wrappers and a boosting-based hybrid for feature selection," Proceedings of the Eighteenth International Conference on Machine Learning, pp. 74-81, 2001.

W. Wettayaprasit, N. Laosen, and S. Chevakidagarn, "Data filtering technique for neural networks forecasting," in Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization: World Scientific and Engineering Academy and Society (WSEAS), pp. 225-230, 2007.

P. Yang, W. Liu, B. B. Zhou, S. Chawla, and A. Y. Zomaya, "Ensemble-based wrapper methods for feature selection and class imbalance learning," in Pacific-Asia conference on knowledge discovery and data mining: Springer, pp. 544-555, 2013.

L. Talavera, "An evaluation of filter and wrapper methods for feature selection in categorical clustering," in International Symposium on Intelligent Data Analysis: Springer, pp. 440-451, 2005.

A. Ghildiyal and S. Sharma, "Music Genre Classification Using Data Filtering Algorithm: An Artificial Intelligence Approach," in 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA): IEEE, pp. 922-926, 2021.

S. Zhang, H. Gu, and R. Li, "Music Genre Classification: Near-Realtime Vs Sequential Approach," PrePrint, 2019.

A. Ghildiyal, K. Singh, and S. Sharma, "Music genre classification using machine learning," in 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA): IEEE, pp. 1368-1372, 2020.

S. Das and A. K. Kolya, "A Theoretic Approach to Music Genre Recognition from Musical Features Using Single-Layer Feedforward Neural Network," in Emerging Technologies in Data Mining and Information Security: Springer, pp. 145-155, 2019.

P. Hamel and D. Eck, "Learning features from music audio with deep belief networks," in ISMIR, vol. 10: Citeseer, pp. 339-344, 2010.

A. Zlatintsi and P. Maragos, "Comparison of different representations based on nonlinear features for music genre classification," in 2014 22nd European Signal Processing Conference (EUSIPCO): IEEE, pp. 1547-1551, 2014.

U. Riaz, S. Aziz, M. Umar Khan, S. A. A. Zaidi, M. Ukasha, and A. Rashid, "A novel embedded system design for the detection and classification of cardiac disorders," Computational Intelligence, vol. 37, no. 4, pp. 1844-1864, 2021.



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Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
ISSN 2338-3070 (print) | 2338-3062 (online)
Organized by Electrical Engineering Department - Universitas Ahmad Dahlan
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