Instance Selection for Music Genre Classification using Heterogeneous Networks

  • Angelo Cesar Mendes da Silva Universidade de São Paulo
  • Paulo Ricardo Viviurka do Carmo Universidade de São Paulo
  • Ricardo Marcondes Marcacini Universidade de São Paulo
  • Diego Furtado Silva Universidade Federal de São Carlos


In scenarios involving musical data, there are usually high-dimensional data and different modalities, such as audio and text, that cost more in machine learning tasks. Instance selection is a promising approach as pre-processing step to reduce these challenges. With the intent to explore the multimodality in music information, we introduce musical data instance selection into heterogeneous network models. We propose and evaluate ten different heterogeneous networks to identify more representative relationships with various musical features related, including songs, artists, genres, and melspectrogram. The results obtained allow us to define which network structure is more appropriate considering the volume of available data and the type of information that the features have. Finally, we analyze the relevance of the musical features, and the relationship does not contribute for instance selection.

Palavras-chave: Music Information Retrieval


Markus Schedl, Emilia Gómez, Julián Urbano, et al. Music information retrieval: Recent developments and applications. Foundations and Trends in Information Retrieval, 8(2-3):127–261, 2014.

YV Murthy and Shashidhar G Koolagudi. Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review. ACM Computing Surveys, 51(3):45, 2018.

Keunwoo Choi, George Fazekas, Mark Sandler, and Kyunghyun Cho. Convolutional recurrent neural networks for music classification. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 2392– 2396, 2016.

Yandre MG Costa, Luiz S Oliveira, and Carlos N Silla Jr. An evaluation of convolutional neural networks for music classification using spectrograms. Applied soft computing, 52:28–38, 2017.

Joan Serrà, Emilia Gómez, and Perfecto Herrera. Audio cover song identification and similarity: background, approaches, evaluation, and beyond. In Advances in Music Information Retrieval, pages 307–332. Springer, 2010.

Diego F. Silva, Chin-Chia M. Yeh, Gustavo E. A. P. A. Batista, and Eamonn Keogh. SiMPle: assessing music similarity using subsequences joins. In International Society for Music Information Retrieval Conference, pages 23–29, 2016.

Jordi Pons, Oriol Nieto, Matthew Prockup, Erik Schmidt, Andreas Ehmann, and Xavier Serra. End-to-end learning for music audio tagging at scale. In International Society for Music Information Retrieval Conference, pages 637– 644, 09 2018.

Christine Dewi, Rung-Ching Chen, Yan-Ting Liu, et al. Similar music instrument detection via deep convolution yolo-generative adversarial network. In 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), pages 1–6. IEEE, 2019.

W. Guo, J. Wang, and S. Wang. Deep multimodal representation learning: A survey. IEEE Access, 7:63373–63394, 2019.

Chao Zhang, Zichao Yang, Xiaodong He, and Li Deng. Multimodal intelligence: Representation learning, information fusion, and applications. IEEE Journal of Selected Topics in Signal Processing, 14(3):478–493, Mar 2020.

Y. Lin, C. Chung, and H. H. Chen. Playlist-based tag propagation for improving music auto-tagging. In 2018 26th European Signal Processing Conference (EUSIPCO), pages 2270–2274, 2018.

Y. Lin and H. Chen. Tag propagation and cost-sensitive learning for music auto-tagging. IEEE Transactions on Multimedia, pages 1–1, 2020.

Angelo Cesar Mendes da Silva, Diego Furtado Silva, and Ricardo Marcondes Marcacini. 4mula: A multitask, multimodal, and multilingual dataset of music lyrics and audio features. In Proceedings of the Brazilian Symposium on Multimedia and the Web, WebMedia ’20, page 145–148, New York, NY, USA, 2020. Association for Computing Machinery.

J. Arturo Olvera-López, J. Ariel Carrasco-Ochoa, J. Francisco Martínez-Trinidad, and Josef Kittler. A review of instance selection methods. Artificial Intelligence Review, 34:133–143, 2010.

Mellish C Brighton H. Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery, 6:153–172, 2002.

van der Aalst Wil Sani Mohammadreza Fani, van Zelst Sebastiaan J. Improving the performance of process discovery algorithms by instance selection. Computer Science and Information Systems, 17:927–958, 2020.

Marek Grochowski and Norbert Jankowski. Comparison of instance selection algorithms ii. results and comments. In Leszek Rutkowski, Jörg H. Siekmann, Ryszard Tadeusiewicz, and Lotfi A. Zadeh, editors, Artificial Intelligence and Soft Computing - ICAISC 2004, pages 580–585, Berlin, Heidelberg, 2004. Springer Berlin Heidelberg.

Jianping Zhang, Yee-Sat Yim, and Junming Yang. Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms, pages 175–191. Springer Netherlands, Dordrecht, 1997.

Chih-Fong Tsai, Wei-Chao Lin, Ya-Han Hu, and Guan-Ting Yao. Under-sampling class imbalanced datasets by combining clustering analysis and instance selection. Information Sciences, 477:47–54, 2019.

Chih-Fong Tsai and Fu-Yu Chang. Combining instance selection for better missing value imputation. Journal of Systems and Software, 122:63–71, 2016.

Joel Luís Carbonera and Mara Abel. Efficient instance selection based on spatial abstraction. In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pages 286–292, 2018.

Peter Hart. The condensed nearest neighbor rule. IEEE transactions on information theory, 14(3):515–516, 1968.

G Ritter, H Woodruff, S Lowry, and T Isenhour. An algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory, 21(6):665–669, 1975.

Joel Luis Carbonera and Mara Abel. A density-based approach for instance selection. In 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), pages 768–774, 2015.

B. Ramesh and J.G.R. Sathiaseelan. An advanced multi class instance selection based support vector machine for text classification. Procedia Computer Science, 57:1124–1130, 2015. 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015).

Junhai Zhai, Xizhao Wang, and Xiaohe Pang. Votingbased instance selection from large data sets with mapreduce and random weight networks. Information Sciences, 367-368:1066–1077, 2016.

Miguel Lopes, Fabien Gouyon, Alessandro L. Koerich, and Luiz E.S. Oliveira. Selection of training instances for music genre classification. In 2010 20th International Conference on Pattern Recognition, pages 4569–4572, 2010.

Ismail M. Anwar, Khalid M. Salama, and Ashraf M. Abdelbar. Instance selection with ant colony optimization. Procedia Computer Science, 53:248–256, 2015. INNS Conference on Big Data 2015 Program San Francisco, CA, USA 8-10 August 2015.

Nicolás García-Pedrajas and Aida de Haro-García. Boosting instance selection algorithms. Knowledge-Based Systems, 67:342–360, 2014.

Aida de Haro-García, Gonzalo Cerruela-García, and Nicolás García-Pedrajas. Instance selection based on boosting for instance-based learners. Pattern Recognition, 96:106959, 2019.

Marcin Blachnik. Ensembles of instance selection methods: A comparative study. International Journal of Applied Mathematics and Computer Science, 29(1):151–168, 2019.

Wenqin Chen, Jessica Keast, Jordan Moody, Corinne Moriarty, Felicia Villalobos, Virtue Winter, Xueqi Zhang, Xuanqi Lyu, Elizabeth Freeman, Jessie Wang, Sherry Kai, and Katherine M. Kinnaird. Data usage in mir: History & future recommendations. In International Society for Music Information Retrieval Conference, 2019.

Sergio Oramas, F. Barbieri, Oriol Nieto, and Xavier Serra. Multimodal deep learning for music genre classification. Transactions of the International Society for Music Information Retrieval, 1:4–21, 2018.

Ngo Tung Son, Duong Xuan Hoa, and Vu Thanh. Learning sparse representation from multiple-source data for relative similarity in music. In Proceedings of the 2018 International Conference on Computational Intelligence and Intelligent Systems, CIIS 2018, page 1–4, New York, NY, USA, 2018. Association for Computing Machinery.

Ho-Hsiang Wu, Chieh-Chi Kao, Qingming Tang, Ming Sun, Brian McFee, Juan Bello, and Chao Wang. Multitask self-supervised pre-training for music classification. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 556–560, 06 2021.

Y.R. Pandeya and J Lee. Deep learning-based late fusion of multimodal information for emotion classification of music video. Multimed Tools Appl, 2020.

Changfeng Chen and Qiang Li. A multimodal music emotion classification method based on multifeature combined network classifier. Mathematical Problems in Engineering, 2020.

Michael I. Mandel and D. Ellis. Multiple-instance learning for music information retrieval. In International Society for Music Information Retrieval, pages 577–582, 2008.

Rachel M Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, and Thor Kell. mirdata: Software for reproducible usage of datasets. In International Society for Music Information Retrieval (ISMIR) Conference, 2019.

Anil K Jain. Data clustering: 50 years beyond k-means. Pattern recognition letters, 31(8):651–666, 2010.

Peter J Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65, 1987.

Dengyong Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. In Advances in neural information processing systems, pages 321–328, 2004.

Bogdan Trawinski, Magdalena Smetek, Zbigniew Telec, and Tadeusz Lasota. Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. Applied Mathematics and Computer Science, 22(4):867–881, 2012. 15CNRS - Univ. Paris 6 & PUC-Rio - France
SILVA, Angelo Cesar Mendes da; CARMO, Paulo Ricardo Viviurka do; MARCACINI, Ricardo Marcondes; SILVA, Diego Furtado. Instance Selection for Music Genre Classification using Heterogeneous Networks. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 18. , 2021, Recife. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 8-16. DOI: