Music Genre Recognition with Handcrafted Audio Features
Resumo
Music has been increasingly prominent in people’s lives in recent years. Technology integration with music is progressively increasing, directly contributing to the enhancement and understanding of this art form. In this sense, Music Genre Recognition (MGR) applies Machine Learning (ML) to identify similar patterns in music and classify them. This study assessed traditional ML algorithms in the GTZAN dataset, classifying audio songs into ten genres. The results showed that the XGBoost classifier statistically outperformed all the other algorithms evaluated, with an accuracy value of 0.722. Future experiments can improve it with a more robust feature engineering process, exploring and mixing deep with traditional features.
Palavras-chave:
Music Informatio Retrieval, Music Genre Recognition, Machine Learning
Referências
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Alonso, M. A., Richard, G., and David, B. (2004). Tempo and beat estimation of musical signals. In International Society for Music Information Retrieval Conference.
Bahuleyan, H. (2018). Music genre classification using machine learning techniques. arXiv preprint arXiv:1804.0114.
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Ghildiyal, A., Singh, K., and Sharma, S. (2020). Music genre classification using machine learning. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 1368–1372.
Haykin, S. S. (2009). Neural networks and learning machines. Pearson Education, third edition.
Livshin, A. (2007). Automatic Musical Instrument Recognition and Related Topics. PhD thesis, Université Pierre et Marie Curie.
McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., and Nieto, O. (2015). librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, volume 8.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill International Editions. McGraw-Hill.
Ndou, N., Ajoodha, R., and Jadhav, A. (2021). Music genre classification: A review of deep-learning and traditional machine-learning approaches. 2021 IEEE International IOT, Eletronics and Mecatronics Conference.
Oppenheim, A. V. and Willsky, A. S. (1997). Signals and Systems. Prentice Hall.
Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the cuidado project.
Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall, 3 edition.
Sehgal, R., Gupta, N., Tomar, A., Sharma, M. D., and Kumaran, V. (2022). Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning. Academic Press.
Shah, M., Pujara, N., Mangaroliya, K., Gohil, L., Vyas, T., and Degadwala, S. (2022). Music genre classification using deep learning. 6th International Conference on Computing Methodologies and Communication (ICCMC).
Shi, L., Li, C., and Tian, L. (2019). Music genre classification based on chroma features and deep learning. In 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP), pages 81–86.
Song, Y., Dixon, S., and Pearce, M. T. (2012). A survey of music recommendation systems and future perspectives. In The 9th International Symposium on Computer Music Modeling and Retrieval (CMMR).
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining. Addison Wesley, us ed edition.
Torres-García, A. A., Garcia, C. A. R., and Luis Villasenor-Pineda, O. M.-M. (2021). Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms, and Applications. Academic Press.
Tzanetakis, G. and Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5).
Tzanetakis, G., Essl, G., and Cook, P. (2001). Automatic musical genre classification of audio signals.
Alonso, M. A., Richard, G., and David, B. (2004). Tempo and beat estimation of musical signals. In International Society for Music Information Retrieval Conference.
Bahuleyan, H. (2018). Music genre classification using machine learning techniques. arXiv preprint arXiv:1804.0114.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. (1984). Classification and Regression Trees. Chapman and Hall/CRC.
Bäckström, T., Räsänen, O., Zewoudie, A., Zarazaga, P. P., Koivusalo, L., Das, S., Mellado, E. G., Mansali, M. B., Ramos, D., Kadiri, S., and Alku, P. (2022). Introduction to Speech Processing. 2 edition.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. arXiv:1603.02754v3.
Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning, 20:273–297.
Ghias, A., Logan, J., Chamberlin, D., and Smith, B. C. (1995). Query by humming: musical information retrieval in an audio database. In MULTIMEDIA ’95.
Ghildiyal, A., Singh, K., and Sharma, S. (2020). Music genre classification using machine learning. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), pages 1368–1372.
Haykin, S. S. (2009). Neural networks and learning machines. Pearson Education, third edition.
Livshin, A. (2007). Automatic Musical Instrument Recognition and Related Topics. PhD thesis, Université Pierre et Marie Curie.
McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., and Nieto, O. (2015). librosa: Audio and music signal analysis in python. In Proceedings of the 14th python in science conference, volume 8.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill International Editions. McGraw-Hill.
Ndou, N., Ajoodha, R., and Jadhav, A. (2021). Music genre classification: A review of deep-learning and traditional machine-learning approaches. 2021 IEEE International IOT, Eletronics and Mecatronics Conference.
Oppenheim, A. V. and Willsky, A. S. (1997). Signals and Systems. Prentice Hall.
Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the cuidado project.
Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall, 3 edition.
Sehgal, R., Gupta, N., Tomar, A., Sharma, M. D., and Kumaran, V. (2022). Smart Electrical and Mechanical Systems: An Application of Artificial Intelligence and Machine Learning. Academic Press.
Shah, M., Pujara, N., Mangaroliya, K., Gohil, L., Vyas, T., and Degadwala, S. (2022). Music genre classification using deep learning. 6th International Conference on Computing Methodologies and Communication (ICCMC).
Shi, L., Li, C., and Tian, L. (2019). Music genre classification based on chroma features and deep learning. In 2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP), pages 81–86.
Song, Y., Dixon, S., and Pearce, M. T. (2012). A survey of music recommendation systems and future perspectives. In The 9th International Symposium on Computer Music Modeling and Retrieval (CMMR).
Tan, P.-N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining. Addison Wesley, us ed edition.
Torres-García, A. A., Garcia, C. A. R., and Luis Villasenor-Pineda, O. M.-M. (2021). Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms, and Applications. Academic Press.
Tzanetakis, G. and Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5).
Tzanetakis, G., Essl, G., and Cook, P. (2001). Automatic musical genre classification of audio signals.
Publicado
17/11/2024
Como Citar
OLIVEIRA, André Luís de; CARVALHO, Luiz Fernando; CAMPOS, Daniel Prado; MANTOVANI, Rafael Gomes.
Music Genre Recognition with Handcrafted Audio Features. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 541-552.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245072.