Comparison of Multiple Machine Learning Algorithms for Music Genre Classification

Research Article
Open access

Comparison of Multiple Machine Learning Algorithms for Music Genre Classification

Diwen Deng 1 , Yiwu Gu 2 , Yiyi Zhu 3*
  • 1 Shanghai University of Engineering Science    
  • 2 East China Normal University    
  • 3 Nanjing University of Aeronautics and Astronautics    
  • *corresponding author zhuyiyi@nuaa.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230220
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

With the fast advance of the Internet and the continuous improvement of computer technology, speech recognition has been applied in many fields, and speech recognition has broad prospects for development. Music and audio classification technology can add category labels to music based on music content, which is of great significance in the research and application of efficient organization, retrieval and recommendation of music resources. In order to efficiently classify audio from massive online music data and help users to obtain the most suitable music style, a deep learning classification algorithm based on convolutional neural network (CNN) is proposed. To examine its effectiveness, it is compared with traditional machine learning algorithm. First, the original music data set was preprocessed and then feature extraction was carried out to obtain music features and transform them into spectral maps. Traditional machine learning model and deep learning component model were used for simulation experiments. The testing accuracy of the deep learning model is up to 92%, verifying the model's superiority.

Keywords:

Music genre classification, machine learning, classification

Deng,D.;Gu,Y.;Zhu,Y. (2023). Comparison of Multiple Machine Learning Algorithms for Music Genre Classification. Applied and Computational Engineering,8,768-774.
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References

[1]. Vishnupriya, S., & Meenakshi, K. (2018). Automatic music genre classification using convolution neural network. In 2018 international conference on computer communication and informatics (ICCCI) , 1-4.

[2]. Xu, Y., & Zhou, W. (2020). A deep music genres classification model based on CNN with Squeeze & Excitation Block. In 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 332-338.

[3]. Matityaho, B., & Furst, M. (1995). Neural network based model for classification of music type. In Eighteenth Convention of Electrical and Electronics Engineers in Israel, 3-4.

[4]. Jiang, D., Lu, L., Zhang, H., Tao, J., & Cai, L. (2002). Music type classification by spectral contrast feature. In Proceedings. IEEE International Conference on Multimedia and Expo, 1, 113-116.

[5]. Costa, Y. M., Oliveira, L. S., Koerich, A. L., Gouyon, F., & Martins, J. G. (2012). Music genre classification using LBP textural features. Signal Processing, 92(11), 2723-2737.

[6]. Sarkar, R., & Saha, S. K. (2015). Music genre classification using EMD and pitch based feature. In 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), 1-6.

[7]. Cahyani, D., & Nuzry, K. (2019). Trending topic classification for single-label using multinomial naive bayes (MNB) and multi-label using k-nearest neighbors (KNN). In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 547-552.

[8]. Liu, Z., Zhang, Q. M., Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A local naïve Bayes model. Europhysics Letters, 96(4), 48007.

[9]. Gang, Z., Shi-kui, P., Hui, R., et, al. (2010). A general introduction to estimation and retrieval of forest volume with remote sensing based on KNN. Remote sensing technology and application, 25(3), 430-437.

[10]. Chillara, S., Kavitha, A. S., Neginhal, S. A., Haldia, S., & Vidyullatha, K. S. (2019). Music genre classification using machine learning algorithms: a comparison. Int Res J Eng Technol, 6(5), 851-858.

[11]. Cokluk, O. (2010). Logistic Regression: Concept and Application. Educational Sciences: Theory and Practice, 10(3), 1397-1407.

[12]. Scabini, L. F., & Bruno, O. M. (2023). Structure and performance of fully connected neural networks: Emerging complex network properties. Physica A: Statistical Mechanics and its Applications, 128585.

[13]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[14]. Li, L. (2021). Learning Recommendation Algorithm Based on Improved BP Neural Network in Music Marketing Strategy. Computational Intelligence and Neuroscience, 1-10.

[15]. Pandeya, Y. R., & Lee, J. (2021). Deep learning-based late fusion of multimodal information for emotion classification of music video. Multimedia Tools and Applications, 80, 2887-2905.

Cite this article

Deng,D.;Gu,Y.;Zhu,Y. (2023). Comparison of Multiple Machine Learning Algorithms for Music Genre Classification. Applied and Computational Engineering,8,768-774.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Vishnupriya, S., & Meenakshi, K. (2018). Automatic music genre classification using convolution neural network. In 2018 international conference on computer communication and informatics (ICCCI) , 1-4.

[2]. Xu, Y., & Zhou, W. (2020). A deep music genres classification model based on CNN with Squeeze & Excitation Block. In 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 332-338.

[3]. Matityaho, B., & Furst, M. (1995). Neural network based model for classification of music type. In Eighteenth Convention of Electrical and Electronics Engineers in Israel, 3-4.

[4]. Jiang, D., Lu, L., Zhang, H., Tao, J., & Cai, L. (2002). Music type classification by spectral contrast feature. In Proceedings. IEEE International Conference on Multimedia and Expo, 1, 113-116.

[5]. Costa, Y. M., Oliveira, L. S., Koerich, A. L., Gouyon, F., & Martins, J. G. (2012). Music genre classification using LBP textural features. Signal Processing, 92(11), 2723-2737.

[6]. Sarkar, R., & Saha, S. K. (2015). Music genre classification using EMD and pitch based feature. In 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), 1-6.

[7]. Cahyani, D., & Nuzry, K. (2019). Trending topic classification for single-label using multinomial naive bayes (MNB) and multi-label using k-nearest neighbors (KNN). In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 547-552.

[8]. Liu, Z., Zhang, Q. M., Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A local naïve Bayes model. Europhysics Letters, 96(4), 48007.

[9]. Gang, Z., Shi-kui, P., Hui, R., et, al. (2010). A general introduction to estimation and retrieval of forest volume with remote sensing based on KNN. Remote sensing technology and application, 25(3), 430-437.

[10]. Chillara, S., Kavitha, A. S., Neginhal, S. A., Haldia, S., & Vidyullatha, K. S. (2019). Music genre classification using machine learning algorithms: a comparison. Int Res J Eng Technol, 6(5), 851-858.

[11]. Cokluk, O. (2010). Logistic Regression: Concept and Application. Educational Sciences: Theory and Practice, 10(3), 1397-1407.

[12]. Scabini, L. F., & Bruno, O. M. (2023). Structure and performance of fully connected neural networks: Emerging complex network properties. Physica A: Statistical Mechanics and its Applications, 128585.

[13]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

[14]. Li, L. (2021). Learning Recommendation Algorithm Based on Improved BP Neural Network in Music Marketing Strategy. Computational Intelligence and Neuroscience, 1-10.

[15]. Pandeya, Y. R., & Lee, J. (2021). Deep learning-based late fusion of multimodal information for emotion classification of music video. Multimedia Tools and Applications, 80, 2887-2905.

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