Machine Learning Based Music Genre Classification and Recommendation System

Year 2022, Volume: 9 Issue: 4, 1560 - 1571, 31.12.2022

Pınar Yılmaz Şeyma Akçakaya Şule Deniz Özkaya Aydın Çetin

https://doi.org/10.31202/ecjse.1209025

Cited By: 2

Abstract

Music has an important role in our life. It is also known that music helps to relax and strengthen the human spirit. The widespread use of the Internet has led to significant changes and developments in the music industry. The increase and widespread use of online music listening and sales platforms, the constant updating of these platforms and the classification of music genres can be given as examples of these developments. Music genre classification is an important step for the music recommendation system. In order for music to be classified by individuals require to listen to many songs and choose their genre. This is a difficult process and waste of time. In this paper, it is aimed to classify music according to its genres by using machine learning algorithms and to suggest similar types of music to the user. For this purpose, the features of the music files were extracted with digital signal processing techniques, and the music genres were automatically detected by using machine learning algorithms with the obtained features and a recommendation system was developed. The GTZAN dataset was chosen to be used in the study. Eight different machine learning models were trained in the Jupyter Notebook environment and the findings were compared. For this purpose, the data set was split into two sets as 80% training and 20% testing, and the accuracy of the models was evaluated. Among the tested models, the most successful result was obtained with the XGBoost algorithm with an accuracy rate of 91,792%.

Keywords

Music, Classification, Recommendation, Genre, Machine Learning

References

• Brown, L. L. "The benefits of music education." PBS KIDS for Parents, 2012.
• İnternet: Number of Apple Music subscribers worldwide from October 2015 to June 2021, 2022, https://www.statista.com/statistics/604959/number-of-apple-music-subscribers/, Erişim Tarihi: Kasım 2022
• İnternet: Number of Spotify premium subscribers worldwide from 1st quarter 2015 to 3rd quarter 2022, 2022, https://www.statista.com/statistics/244995/number-of-paying-spotify-subscribers/ Erişim Tarihi: Kasım 2022
• İnternet: Spotify Investors Quick Facts, 2021, https://investors.spotify.com/home/default.aspx, Youtube Offical Blog, 2020, https://blog.youtube/news-and-events/youtube-music-transfer-google-play-music-library/, Erişim Tarihi: Ocak 2022
• İnternet: The 5 best music streaming services you can subscribe to in 2022, https://www.businessinsider.com/guides/tech/best-music-streaming-service-subscription, Erişim Tarihi: Kasım 2022
• İnternet: Music Genres List, https://www.musicgenreslist.com/, Erişim Tarihi: Kasım 2022
• Tzanetakis, G., Cook, P. “Musical genre classification of audio signal”, IEEE Transactions on Speech and Audio Processing, Vol. 10, No. 3, pp. 293-302, July 2002.
• Li, T., Ogihara, M., Li, Q. "A comparative study on content-based music genre classification", SIGIR '03 Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 282-289, 2003.
• Karatana, A., Yildiz, O. “Music genre classification with machine learning techniques,” in Signal Processing and Communications Applications Conference (SIU), 2017 25th, IEEE, 2017, pp. 1–4.
• Bergstra, J., Casagrande, N., Erhan, D., Eck, D., Kegl, B. “Aggregate features and AdaBoost for music classification”, Machine Learning, Vol. 65, No. 2-3, pp. 473-484, 2006.
• Benetos, E., Kotropoulos C. “A tensor-based approach for automatic music genre classification”, Proceedings of te European Signal Processing Conference, Lausanne, Switzerland, 2008
• İnternet: Tensorflow GTZAN Description, 2021, https:// www.tensorflow.org/datasets/catalog/gtzan, Erişim Tarihi: Ocak 2022
• Banitalebi-Dehkordi, M., Banitalebi-Dehkordi, A. “Music Genre Classification Using Spectral Analysis and Sparse4Representation of the Signals” ,Computer Science, Engineering,Journal of Signal Processing Systems ,Published 1 February 2014
• Dan Ellis,Chroma Feature Analysis and Synthesis, 2007/04/21, Published with MATLAB®7.3
• McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., Nieto, O. "librosa: Audio and Music Signal Analysis in Python", Proc. Of the 14th python in science conf. (SCIPY 2015), 2015.
• Webb, G.I. (2011). Naïve Bayes. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_576
• Freund, Y. and Schapire, R.E., A Short Introduction to Boosting, Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, 1990
• K. Taunk, S. De, S. Verma and A. Swetapadma, "A Brief Review of Nearest Neighbor Algorithm for Learning and Classification," 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 2019, pp. 1255-1260, doi: 10.1109/ICCS45141.2019.9065747.
• Jair Cervantes, Farid Garcia-Lamont, Lisbeth Rodríguez-Mazahua, Asdrubal Lopez, A comprehensive survey on support vector machine classification: Applications, challenges and trends, Neurocomputing, Volume 408, 2020,Pages 189-215,
• L. Liu, "Research on Logistic Regression Algorithm of Breast Cancer Diagnose Data by Machine Learning," 2018 International Conference on Robots & Intelligent System (ICRIS), 2018, pp. 157-160, doi: 10.1109/ICRIS.2018.00049
• J. K. Jaiswal and R. Samikannu, "Application of Random Forest Algorithm on Feature Subset Selection and Classification and Regression," 2017 World Congress on Computing and Communication Technologies (WCCCT), 2017, pp. 65-68, doi: 10.1109/WCCCT.2016.25.
• A. A. Karcioğlu and H. Bulut, "Performance Evaluation of Classification Algorithms Using Hyperparameter Optimization," 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021, pp. 354-358, doi: 10.1109/UBMK52708.2021.9559003.
• N. Fazakis, G. Kostopoulos, S. Karlos, S. Kotsiantis and K. Sgarbas, "Self-trained eXtreme Gradient Boosting Trees," 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), 2019, pp. 1-6, doi: 10.1109/IISA.2019.8900737.
• İnternet: TIBCO Jupyter Notebooks Requirements, https://docs.tibco.com/pub/sfire-dsc/6.5.0/doc/html/TIB_sfire-dsc_sys-req/GUID-291ABBD3-9DC6-4659-8595-3F208F24565A.html, Erişim Tarihi: Ocak 2022