Daniel Kostrzewa
ABSTRACT
The possibility of classifying musical songs according to their musical genre is becoming more challenging because of the millions of songs included in online databases. Therefore, reliable and efficient methods need to be developed that will automatically solve this task. In this article, the mentioned task is accomplished using sets of classifiers. The authors' contribution to the development of automatic musical genre recognition is using hybrid ensembles formed from deep neural networks and classical classifiers and the optimization process executed on the voting process of individual classifiers. Finally, differential evolution algorithms have been used to improve the classification quality further. The proposed evolutionary algorithm shows improvement in comparison with other optimization methods.
- Hareesh Bahuleyan. 2018. Music genre classification using machine learning techniques. arXiv preprint arXiv:1804.01149 (2018).Google Scholar
- Keunwoo Choi, György Fazekas, Mark Sandler, and Kyunghyun Cho. 2017. Convolutional recurrent neural networks for music classification. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2392--2396.Google ScholarDigital Library
- Yandre MG Costa, Luiz S Oliveira, and Carlos N Silla Jr. 2017. An evaluation of convolutional neural networks for music classification using spectrograms. Applied soft computing 52 (2017), 28--38.Google Scholar
- Michaël Defferrard, Kirell Benzi, Pierre Vandergheynst, and Xavier Bresson. 2017. FMA: A Dataset for Music Analysis. In 18th International Society for Music Information Retrieval Conference (ISMIR). arXiv:1612.01840 https://arxiv.org/abs/1612.01840Google Scholar
- Alican Dogan and Derya Birant. 2019. A weighted majority voting ensemble approach for classification. In 2019 4th International Conference on Computer Science and Engineering (UBMK). IEEE, 1--6.Google ScholarCross Ref
- Mingwen Dong. 2018. Convolutional neural network achieves human-level accuracy in music genre classification. arXiv preprint arXiv:1802.09697 (2018).Google Scholar
- Lin Feng, Shenlan Liu, and Jianing Yao. 2017. Music genre classification with paralleling recurrent convolutional neural network. arXiv preprint arXiv:1712.08370 (2017).Google Scholar
- Daniel Kostrzewa, Piotr Kaminski, and Robert Brzeski. 2021. Music Genre Classification: Looking for the Perfect Network. In International Conference on Computational Science. Springer, 55--67.Google Scholar
- Loris Nanni, Yandre MG Costa, Alessandra Lumini, Moo Young Kim, and Seung Ryul Baek. 2016. Combining visual and acoustic features for music genre classification. Expert Systems with Applications 45 (2016), 108--117.Google ScholarDigital Library
- Loris Nanni, Gianluca Maguolo, Sheryl Brahnam, and Michelangelo Paci. 2021. An ensemble of convolutional neural networks for audio classification. Applied Sciences 11, 13 (2021), 5796.Google ScholarCross Ref
- Aytuğ Onan, Serdar Korukoğlu, and Hasan Bulut. 2016. A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Systems with Applications 62 (2016), 1--16.Google ScholarDigital Library
- Jordi Pons and Xavier Serra. 2019. Randomly weighted cnns for (music) audio classification. In ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 336--340.Google ScholarCross Ref
- Kenneth V Price. 2013. Differential evolution. In Handbook of optimization. Springer, 187--214.Google Scholar
- Jaime Ramírez and M Julia Flores. 2020. Machine learning for music genre: multifaceted review and experimentation with audioset. Journal of Intelligent Information Systems 55, 3 (2020), 469--499.Google ScholarDigital Library
- Amanda Ross and Victor L Willson. 2017. Paired samples T-test. In Basic and advanced statistical tests. Brill Sense, 17--19.Google Scholar
- Daniel Silver, Monica Lee, and C Clayton Childress. 2016. Genre complexes in popular music. PloS one 11, 5 (2016), e0155471.Google ScholarCross Ref
- Rainer Storn and Kenneth Price. 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 4 (1997), 341--359.Google ScholarDigital Library
- Chris Thornton, Frank Flutter, Holger H Hoos, and Kevin Leyton-Brown. 2013. Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 847--855.Google ScholarDigital Library
Index Terms
- Evolvable hybrid ensembles for musical genre classification
Recommendations
Music Genre Classification and Feature Comparison using ML
ICMLT '22: Proceedings of the 2022 7th International Conference on Machine Learning TechnologiesAn essential feature of the music is the genre, which can be considered a high-level description of an individual piece of music. In this sense, genre as a music feature is similar to typical descriptive features from the ML perspective. Although a ...
Music Genre Classification with Transformer Classifier
ICDSP '20: Proceedings of the 2020 4th International Conference on Digital Signal ProcessingMusic Genre Classification is a significant and practical field of Music Information Retrieval. Deep learning is increasingly being applied to Music Genre Classification for two main reasons. Firstly, it avoids the manual selection of audio signal ...
Detecting Musical Genre Borders for Multi-label Genre Classification
ISM '13: Proceedings of the 2013 IEEE International Symposium on MultimediaIn this paper, we propose a novel method to detect music genre borders for the music genre classification. The music genre classification is getting more important because music is influenced by an increasing amount of different musical styles. A ...
Comments