Abstract
In this paper, we propose a new approach for automatic music genre classification which relies on learning a feature hierarchy with a deep learning architecture over hand-crafted feature extracted from an audio signal. Unlike the state-of-the-art approaches, our scheme uses an unsupervised learning algorithm based on Deep Belief Networks (DBN) learnt on block-wise MFCC (that we treat as 2D images), followed by a supervised learning algorithm for fine-tuning the extracted features. Experiments performed on the GTZAN dataset show that the proposed scheme clearly outperforms the state-of-the-art approaches.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Tzanetakis, G.: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 10(5), 293–302 (2002)
Lidy, T., Rauber, A.: Evaluation of feature extractors and psycho-acoustic transformations for music genre classification. In: International Society for Music Information Retrieval Conference, pp. 34–41 (2005)
Tsuji, Y., Akahori, K., Nishikata, A.: The estimation of music genre using neural network and its educational use. In: International Conference on Computer-Assisted Instruction, pp. 158–162 (2000)
Bergstra, J., Kgl, B.: Aggregate features and adaboost for music classification. Machine Learning 2(65), 473–484 (2006)
Seyerlehner, K., Schedl, M., Pohle, T., Knees, P.: Using block-level features for genre classification, tag, classification and music similarity estimation. In: IMEX (2010)
Costa, Y., Oliveira, L., Koerich, A., Gouyon, F.: Music genre recognition using spectograms. In: WSSIP 2010, pp. 151–154 (2010)
Hua, B., Fu-long, M., Li-cheng, J.: Research on computation of glcm of image texture (2006)
Li, T.L., Chan, A., Chun, A.: Automatic musical pattern feature extraction using convolutional neural network. In: IMECS 2010 (2010)
Hinton, G.: To recognize shapes, first learn to generate images. Progress in Brain Research 165, 535–547 (2006)
Hamel, P., Eck, D.: Learning features from music audio with deep belief networks. In: International Society for Music Information Retrieval, pp. 339–344 (2010)
Ranzato, M., Boureau, Y.-L., Chopra, S., Lecun, Y.: A unified energy-based framework for unsupervised learning. Journal of Machine Learning Research 2, 371–379 (2007)
Bridle, J., Brown, M.: An experimental word recognition system, jsru report no 1003. Joint Speech Research Unit, Ruislip, England, Tech. Rep. (1974)
Li, T.L., Chan, A.: Genre classification and the invariance of mfcc features to key and tempo. In: International Conference on MultiMedia Modeling (2011)
Li, T.L., Tzanetakis, G.: Factors in automatic musical genre classification. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (2003)
Chang, K., Jang, J., Ilioupoulos, C.: Music genre classification via compressive sampling. In: International Society for Music Information Retrieval, pp. 387–392 (2010)
Panagakis, Y., Kotropoulos, C., Arce, G.: Music genre classification using locality preserving non-negative tensor factorization and sparse representations. In: International Society for Music Information Retrieval, pp. 249–254 (2009)
Henaff, M., Jarett, K., Kavukcuoglu, K., LeCun, Y.: Unsupervised learning of sparse features for scalable audio classification. In: International Society for Music Information Retrieval (2011)
Li, T.L., Ogihara, M., Li, Q.: A comparative study on content-based music genre classification. In: ACM SIGIR Conference on Research and Development in Information Retrieval (2003)
Bergstra, J., Mandel, M., Eck, D.: Scalable genre and tag prediction using spectral covariance. In: International Society for Music Information Retrieval (2010)
Smith, E., Lewicki, M.: Efficient auditory coding. Nature (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martel, J., Nakashika, T., Garcia, C., Idrissi, K. (2013). A Combination of Hand-Crafted and Hierarchical High-Level Learnt Feature Extraction for Music Genre Classification. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)