Abstract
Automatic music genre classification systems are vital nowadays because the traditional music genre classification process is mostly implemented without following a universal taxonomy and the traditional process for audio indexing is prone to error. Various techniques to implement an automatic music genre classification system can be found in the literature but the accuracy and efficiency of those systems are insufficient to make them useful for practical scenarios such as identifying songs by the music genre in radio broadcast monitoring systems. The main contribution of this research is to increase the accuracy and efficiency of current automatic music genre classification systems with a comprehensive analysis of correlations between the descriptive statistical features of audio signals and the music genres of songs. A greedy approach for music genre identification is also introduced to improve the accuracy and efficiency of music genre classification systems and to identify the music genre of complex songs that contain multiple music genres. The approach, proposed in this paper, reported 87.3% average accuracy for music genre classification on the GTZAN dataset over 10 music genres.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jeong, Y., Lee, K.: Learning temporal features using a deep neural network and its application to music genre classification. In: 17th International Society for Music Information Retrieval Conference (ISMIR), New York (2016)
Scaringella, N., Zoia, G., Mlynek, D.: Automatic genre classification of music content: a survey. In: IEEE Signal Processing Magazine, vol. 22 no. 2 (2006)
Anan, Y., Hatano, K., Bannai, H., Takeda, M.: Music genre classification using similarity functions. In: 12th International Society for Music Information Retrieval Conference (ISMIR 2011) (2011)
Arora, V., Kumar, R.: Probability distribution estimation of music signals in time and frequency domains. In: 19th International Conference on Digital Signal Processing, Hong Kong (2014)
Corrêa, D.C., Rodrigues, F.A.: A survey on symbolic data-based music genre classification. Expert Syst. Appl. Int. J. vol. 6, no. C (2016)
Fu, Z., Lu, G., Ting, K.M., Zhang, D.: A survey of audio-based music classification and annotation. IEEE Trans. Multimedia 13(2), 303–319 (2011)
Tzanetakis, G., Cook, P.R.: Musical genre classification of audio signals. In: IEEE Transactions on Speech and Audio Processing (2002)
Mckay, C., Fujinaga, I.: Musical genre classification: is it worth pursuing and how can it be improved? In: 7th International Conference on Music Information Retrieval (ISMIR 2006) (2006)
Serrà , J., Gómez, E., Herrera, P.: Audio cover song identification and similarity: background, approaches, evaluation, and beyond. Stud. Comput. Intell. 273, 307–332 (2010)
Anglade, A., Benetos, E., Dixon, S.: Improving music genre classification using automatically induced harmony rules. J. New Music Res. 39, 349–361 (2010)
Chathuranga, D., Jayaratne, K.L.: Musical genre classification using ensemble of classifiers. In: Fourth International Conference on Computational Intelligence, Modelling and Simulation (2012)
Chathuranga, D., Jayaratne, L.: Automatic music genre classification of audio signals with machine learning approaches. GSTF J. Comput. (JoC) 3(2), 1–12 (2013). https://doi.org/10.7603/s40601-013-0014-0
Nirmal, M.R., Shajee Mohan, B.S.: Music Genre Classification Using Spectrograms. In: International Conference on Power, Instrumentation, Control and Computing (PICC) (2020)
Bahuleyan, H.: Music genre classification using machine learning techniques. ArXiv abs/1804.01149 (2018)
Deepanway, G., Kolekar, M.H.: Music genre recognition using deep neural networks and transfer learning. In: Interspeech 2018 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Howard, A., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications arxiv:1704.04861 (2017)
Hennequin, R., Khlif, A., Voituret, F., Moussallam, M.: Spleeter: a fast and efficient music source separation tool with pre-trained models. J. Open Source Software 5, 2154 (2020)
Giannakopoulos, T.: pyAudioAnalysis: an open-source python library for audio signal analysis. PLoS ONE 10 (2015)
McFee, B., Raffel, C., Liang, D., Ellis, D.: librosa: audio and music signal analysis in python. In: Python in Science Conference (2015)
Karunarathna, G.A.G.S., Jayaratne, K.L., Gunawardana, P.V.K.G.: Classification of voice content in the context of public radio broadcasting. Int. J. Adv. ICT Emerg. Reg. (ICTer) 12(2) (2019)
Weerathunga, C.O.B., Jayaratne, K.L., Gunawardana, P.V.K.G.: Classification of public radio broadcast context for onset detection. In: European Journal of Computer Science and Information Technology (EJCSIT) by European Centre for Research Training and Development UK, 7(6):1–22 (2019)
Tzanetakis, G., Cook, P.: MARSYAS: a framework for audio analysis. Organ. Sound 4(3), 169–175 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Perera, R., Wickramasinghe, M., Jayaratne, L. (2023). Improving Automatic Music Genre Classification Systems by Using Descriptive Statistical Features of Audio Signals. In: Johnson, C., RodrÃguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2023. Lecture Notes in Computer Science, vol 13988. Springer, Cham. https://doi.org/10.1007/978-3-031-29956-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-29956-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29955-1
Online ISBN: 978-3-031-29956-8
eBook Packages: Computer ScienceComputer Science (R0)