Research on music genre recognition method based on deep learning
Abstract
In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.
References
1. Kosina K. Music genre recognition. Media Technology and Design (MTD). Upper Austria University of Applied Sciences Ltd, Hagenberg; 2002.
2. Choi K, Fazekas G, Sandler M, et al. Convolutional recurrent neural networks for music classification. In: Proceeding of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2017. doi: 10.1109/icassp.2017.7952585
3. Ndou N, Ajoodha R, Jadhav A. Music Genre Classification: A Review of Deep-Learning and Traditional Machine-Learning Approaches. In: Proceeding of the 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS); 2021. doi: 10.1109/iemtronics52119.2021.9422487
4. Bahuleyan H. Music genre classification using machine learning techniques. Cornell University; 2018.
5. Zheng S, Zhou X, Zhang L, et al. Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification. IEEE Transactions on Cognitive Communications and Networking. 2023; 9(3): 564-579. doi: 10.1109/tccn.2023.3243899
6. Schluter J, Bock S. Improved musical onset detection with Convolutional Neural Networks. In: Proceeding of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2014.pp. 9-6983. doi: 10.1109/icassp.2014.6854953
7. Pons J, Lidy T, Serra X. Experimenting with musically motivated convolutional neural networks. In: Proceeding of the 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI); 2016. doi: 10.1109/cbmi.2016.7500246
8. Lim M, Lee D, Park H, et al. Convolutional Neural Network based Audio Event Classification. KSII Transactions on Internet & Information Systems. 2018.
9. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553): 436-444. doi: 10.1038/nature14539
10. Pons J, Serra X. Randomly Weighted CNNs for (Music) Audio Classification. In: Proceeding of the ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi: 10.1109/icassp.2019.8682912
11. Nanni L, Maguolo G, Brahnam S, et al. An Ensemble of Convolutional Neural Networks for Audio Classification. Applied Sciences. 2021; 11(13): 5796. doi: 10.3390/app11135796
12. Zhang Y. Music Recommendation System and Recommendation Model Based on Convolutional Neural Network. Wu CH, ed. Mobile Information Systems. 2022; 2022: 1-14. doi: 10.1155/2022/3387598
13. Oramas S, Nieto O, Barbieri F, et al. Multi-label music genre classification from audio, text, and images using deep features. Cornell University; 2017.
14. Costa YM, Oliveira LS, Koericb AL, et al. Music genre recognition using spectrograms. In: Proceeding of the 2011 18th International conference on systems, signals and image processing; 2011.
15. Sturm BL. A survey of evaluation in music genre recognition,” in International Workshop on Adaptive Multimedia Retrieval. Springer; 2012. pp. 29-66.
16. Lee J, Nam J. Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging. IEEE Signal Processing Letters. 2017; 24(8): 1208-1212. doi: 10.1109/lsp.2017.2713830
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