Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection

Abstract

Music holds a significant role in our daily lives, and its impact on emotions has been a focal point of research across various disciplines, including psychology, sociology, and statistics. Ongoing studies continue to explore this intriguing relationship. With advancing technology, the ability to choose from a diverse range of music has expanded. Recent trends highlight a growing preference for searching for music based on emotional attributes rather than individual preferences or genres. The act of selecting music based on emotional states is important on both a universal and cultural level. This study seeks to employ machine learning-based methods to classify four different music genres using a minimal set of features. The objective is to facilitate the process of choosing Turkish music according to one’s mood. The classification methods employed include Decision Tree, Random Forest (RF), Support Vector Machines (SVM), and k-Nearest Neighbor, coupled with the Mutual Information (MI) feature selection algorithm. Experimental results reveal that, with all features considered in the dataset, RF achieved the highest accuracy at 0.8098. However, when the MI algorithm was applied, SVM exhibited the best accuracy at 0.8068. Considering both memory consumption and accuracy, the RF method emerges as a favorable choice for selecting Turkish music based on emotional states. This research not only advances our understanding of the interaction between music and emotions but also provides practical insights for individuals who want to shape their music according to their emotional preferences.

Keywords

• Classification
• emotions
• feature selection
• music genres
• mutual information

References

1. C. Ji, J. Zhao, Q. Nie, S. Wang, The role and outcomes of music therapy during pregnancy: a systematic review of randomized controlled trials, Journal of Psychosomatic Obstetrics & Gynecology 45 (1) (2024) 2291635 10 pages.
2. G. Leslie, A. Ghandeharioun, D. Zhou, R. W. Picard, Engineering music to slow breathing and invite relaxed physiology, 8th international conference on Affective Computing and Intelligent Interaction (ACII), Cambridge, 2019, pp.1–7.
3. M. Umbrello, T. Sorrenti, G. Mistraletti, P. Formenti, D. Chiumello, S. Terzoni, Music therapy reduces stress and anxiety in critically ill patients: a systematic review of randomized clinical trials, Minerva Anestesiologica 85 (8) (2019) 886–898.
4. P. Vuust, O. A. Heggli, K. J. Friston, M. L. Kringelbach, Music in the brain, Nature Reviews Neuroscience 23 (5) (2022) 287–305.
5. B. M. O. Shimada, M. A. Cabral, V. O. Silva, G. C. Vagetti, Interventions among pregnant women in the field of music therapy: A systematic review, Revista Brasileira de Ginecologia e Obstetrícia/RBGO Gynecology and Obstetrics 43 (05) (2021) 403–413.
6. D. Han, Y. Kong, J. Han, G. Wang, A survey of music emotion recognition, Frontiers of Computer Science 16 (6) (2022) 166335 11 pages.
7. T. Fritz, S. Jentschke, N. Gosselin, D. Sammler, I. Peretz, R. Turner, A. D. Friederici, S. Koelsch, Universal recognition of three basic emotions in music, Current Biology 19 (7) (2009) 573–576.
8. A. Mahadik, S. Milgir, J. Patel, V. B. Jagan, V. Kavathekar, Mood based music recommendation system, International Journal of Engineering Research & Technology (IJERT) 10 (6) (2021) 553–559.

9. A. O. Durahim, A. C. Setirek, B. B. Özel, H. Kebapci, Music emotion classification for Turkish songs using lyrics, Pamukkale University Journal of Engineering Sciences 24 (2) (2018) 292–301.
10. M. B. Er, I. B. Aydilek, Music emotion recognition by using chroma spectrogram and deep visual features, International Journal of Computational Intelligence Systems 12 (2) (2019) 1622–1634.
11. D. Chaudhary, N. P. Singh, S. Singh, Development of music emotion classification system using convolution neural network, International Journal of Speech Technology 24 (2021) 571–580.
12. M. T. Quasim, E. H. Alkhammash, M. A. Khan, M. Hadjouni, RETRACTED ARTICLE: Emotion-based music recommendation and classification using machine learning with IoT Framework, Soft Computing 25 (18) (2021) 12249–12260.
13. J. H. Su, T. P. Hong, Y. H. Hsieh, S. M. Li, Effective music emotion recognition by segment-based progressive learning, 2020 IEEE International Conference on Systems, Man, and Cybernetics, Toronto, 2020, pp. 3072–3076.
14. A. G. Pandrea, J. S. Gómez Cañón, H. Boyer, Cross-dataset music emotion recognition: an end-to-end approach, in: J. Cumming, J. H. Lee, B. McFee, M. Schedl, J. Devaney, C. McKey, E. Zangerle, T de Reuse (Eds.), 21st International Society for Music Information Retrieval Conference, Québec, 2020, 2 pages.
15. T. Ciborowski, S. Reginis, D. Weber, A. Kurowski, B. Kostek, Classifying emotions in film music—A deep learning approach, Electronics 10 (23) (2021) 2955 22 pages.
16. Z. Huang, S. Ji, Z. Hu, C. Cai, J. Luo, X. Yang, ADFF: Attention based deep feature fusion approach for music emotion recognition (2022) 5 pages, https://doi.org/10.48550/arXiv.2204.05649.
17. K. Zhang, X. Wu, R. Tang, Q. Huang, C. Yang, H. Zhang, The JinYue database for huqin music emotion, scene and imagery recognition, in: L. O’ Conner (Ed.), In 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR), Tokyo, 2021, pp. 314–319.
18. D. Moldovan, Binary horse optimization algorithm for feature selection, Algorithms 15 (5) (2022) 156 27 pages.
19. W. Feng, J. Gou, Z. Fan, X. Chen, An ensemble machine learning approach for classification tasks using feature generation, Connection Science 35 (1) (2023) 2231168 23 pages.
20. M. Çakır, A. Degirmenci, O. Karal, Exploring the behavioural factors of cervical cancer using ANOVA and machine learning techniques, in: J. Filipe, A. Ghosh, R. O. Prates, B. Horizonte, L. Zhou (Eds.), International Conference on Science, Engineering Management and Information Technology, Ankara, 2022, pp. 249–260.
21. H. Li, B. Wan, D. Chu, R. Wang, G. Ma, J. Fu, Z. Xiao, Progressive geological modeling and uncertainty analysis using machine learning, ISPRS International Journal of Geo-Information 12 (3) (2023) 97 19 pages.
22. J. Gonzalez-Lopez, S. Ventura, A. Cano, Distributed multi-label feature selection using individual mutual information measures, Knowledge-Based Systems 188 (2020) 105052 13 pages.
23. P. Zhang, G. Liu, J. Song, MFSJMI: Multi-label feature selection considering join mutual information and interaction weight, Pattern Recognition 138 (2023) 109378.
24. J. R. Vergara, P. A. Estévez, A review of feature selection methods based on mutual information, Neural Computing and Applications 24 (2014) 175–186.
25. A. Degirmenci, O. Karal, iMCOD: Incremental multi-class outlier detection model in data streams, Knowledge-Based Systems 258 (2022) 109950.
26. R. Suwanda, Z. Syahputra, E. M. Zamzami, Analysis of Euclidean distance and Manhattan distance in the K-means algorithm for variations number of centroid K, Journal of Physics: Conference Series 1566 (1) (2020) 012058 6 pages.
27. A. N. Karaoglu, H. Caglar, A. Degirmenci, O. Karal, Performance improvement with decision tree in predicting heart failure, 6th International Conference on Computer Science and Engineering (UBMK), Ankara, 2021, 781– 784.
28. S. Tangirala, Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm, International Journal of Advanced Computer Science and Applications 11 (2) (2020) 612–619.
29. M. A. Bouke, A. Abdullah, S. H. ALshatebi, M. T. Abdullah, H. El Atigh, An intelligent DDoS attack detection tree-based model using Gini index feature selection method, Microprocessors and Microsystems 98 (2023) 104823.
30. S. Yao, Y. Wu, F. Akter, An introduction to artificial intelligence and machine learning, in: F. Akter, N. Emptage, F. Engert, M. S. Berger (Eds.), Neuroscience for Neurosurgeons, Cambridge University Press, 2023, Ch. 9, pp.146–157.
31. R. Mirzaeian, R. Nopour, Z. Asghari Varzaneh, M. Shafiee, M. Shanbehzadeh, H. Kazemi-Arpanahi, Which are best for successful aging prediction? Bagging, boosting, or simple machine learning algorithms?, Biomedical Engineering Online 22 (1) (2023) 85 25 pages.
32. M. Apaydın, M. Yumuş, A. Değirmenci, Ö. Karal, Evaluation of air temperature with machine learning regression methods using Seoul City meteorological data, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 28 (5) (2022) 737–747.
33. T. M. Oshiro, P. S. Perez, J. A. Baranauskas, How many trees in a random forest?, in: P. Perner (Ed.), Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, Berlin, 2012, 154–168.
34. P. Probst, A. L. Boulesteix, To tune or not to tune the number of trees in random forest, The Journal of Machine Learning Research 18 (1) (2017) 6673–6690.
35. C. B. Liu, B. P. Chamberlain, D. A. Little, Â. Cardoso, Generalising random forest parameter optimisation to include stability and cost, in: M. Ceci, J. Hollmén, L. Todorovski, C. Vens, S. Džeroski (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, Skopje, 2017, 102–113.
36. M. Muttaqi, A. Degirmenci, O. Karal, US accent recognition using machine learning methods, Innovations in Intelligent Systems and Applications Conference (ASYU), Antalya, 2022, 1–6.
37. M. Sheykhmousa, M. Mahdianpari, H. Ghanbari, F. Mohammadimanesh, P. Ghamisi, S. Homayouni, Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13 (2020) 6308–6325.