Skip to main content
SpringerLink
Log in

Genre-Based Classification of Song Using Perceptual Features

  • Conference paper
Intelligent Computing, Networking, and Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 243))

Abstract

Genre-based classification of song is one of the major steps in the music retrieval system. In this work, we have presented perception-based song genre classification. Many of the past researchers have been using combination of perception-based features and other popular features such as zero-crossing, short-time energy. We have used three perceptual features that capture the ordering of sound in frequency scale (pitch-based features), the pace of a musical piece (tempo-based features), and repetition of a pattern in the audio signal. In order to capture the repeating pattern in a signal, we have used cooccurrence matrix. The experimental result using multilayer perceptron network as a classifier indicates the effectiveness of our proposed scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Scaringella, N., Zoia, G.: On the modeling of time information for automatic genre recognition systems in audio signals. In: Proceedings of the 6th International Symposium on Music Information Retrieval, London, UK (2005)

    Google Scholar 

  2. Ghosal, A., Chakraborty, R., Dhara, B.C., Saha, S.K.: Music classification based on MFCC variants and amplitude variation pattern: a hierarchical approach. Int. J. Sig. Process. Image Process. Pattern Recogn. 5(1) (2012)

    Google Scholar 

  3. West, K., Cox, S.: Features and classifiers for the automatic classification of musical audio signals. In: International Symposium on Music Information Retrieval (2004)

    Google Scholar 

  4. Aucouturier, J.J., Pachet, F.: Improving timbre similarity: how highs the sky? J. Negative Results Speech Audio Sci. 1(1), 1 (2004)

    Google Scholar 

  5. Logan, B., Salomon, A.: A music similarity function based on signal analysis. In: ICME 2001, Tokyo, Japan (2001)

    Google Scholar 

  6. Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)

    Article  Google Scholar 

  7. Whitman, B., Flake, G., Lawrence, S.: Artist detection in music with minnow match. In: IEEE Workshop on Neural Networks for Signal Processing, pp. 559–568. Falmouth, Massachusetts, 10–12 September 2001

    Google Scholar 

  8. Xu, C., Maddage, N.C., Shao, X., Cao, F., Tian, Q.: Musical genre classification using support vector machines. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (2003)

    Google Scholar 

  9. Moreno, P.J., Ho, P.P., Vasconcelos, N.: A kullback-leibler divergence based kernel for SVM classification in multimedia applications. In: Thrun, S., Saul, L., Scholkopf, B. (eds.) Advances in Neural Information Processing Systems 16. MIT Press, Cambridge (2004)

    Google Scholar 

  10. Solatu, H., Schultz, T., Westphal, M., Waibel, A.: Recognition of music types. In: IEEE Conference on Acoustics, Speech and Signal Processing, pp. 1137–1140 (1998)

    Google Scholar 

  11. Scheirer, E.: Tempo and beat analysis of acoustic musical signals. J. Acoust. Soc. Am. 103, 588 (1998)

    Article  Google Scholar 

  12. Laroche, J.: Estimating tempo, swing and beat locations in audio recordings. In: Workshop on Application of Signal Processing to Audio and Acoustics (WASPAA) (2001)

    Google Scholar 

  13. Foote, J.T., Uchihashi, S., The beat spectrum: a new approach to rhythmic analysis. In: IEEE International Conference on Multimedia and Expo (ICME) (2001)

    Google Scholar 

  14. Lin, C.R., Liu, N.H., wu, Y.H., Chen, A.L.P.: Music classification using significant repeating patterns. In: LNCS, vol. 2973, pp. 506–518 (2004)

    Google Scholar 

  15. Lo, Y.L., Lin, Y.C.: Content-based music classification. In: International Conference on Computer Science and Information Technology, vol. 2, pp. 112–116 (2010)

    Google Scholar 

  16. Grimaldi, M., Cunningham, P., Kokaram, A.: An evaluation of alternative feature selection strategies and ensemble techniques for classifying music. In: Workshop on Multimedia Discovery and Mining (2003)

    Google Scholar 

  17. Jiang, D.N., Lu, L., Zhang, H.J., Tao, J.H., Cai, L.H.: Music type classification by spectral contrast feature. In: IEEE International Conference on Multimedia and Expo (ICME) (2002)

    Google Scholar 

  18. Lidy, T., Rauber, A.: Evaluation of feature extractors and psycho-acoustic transformations for music genre classification. In: Proceedings of the 6th International Symposium on Music Information Retrieval, London, UK (2005)

    Google Scholar 

  19. Lee, C.H., Lin, H.S., Chou, C.H., Shih, J.L.: Modulation spectral analysis of static and transitional information of cepstral and spectral features for music genre classification. In: International Conference on Intelligent Hiding and Multimedia Signal Processing, pp. 1030–1033 (2009)

    Google Scholar 

  20. Zhen, C., Xu, J.: Multi-modal music genre classification approach. In: International Conference on Computer Science and Information Technology, vol. 8, pp. 398–402 (2010)

    Google Scholar 

  21. Garcia, D.G., Garcia, J.A., Hernandez, F.D., Maria, F.D.: Music genre classification using temporal structure of songs. In: IEEE International Workshop on Machine Learning for Signal Processing, pp. 266–271 (2010)

    Google Scholar 

  22. Simsekli, U.: Automatic music genre classification using bass lines. In: International Conference on Pattern Recognition, pp. 4137–4140 (2010)

    Google Scholar 

  23. Costa, Y.M.G., Oliveira, L.S., Koreich, A.L., Gouyon, F.: Music genre recognition using spectrograms. In: International Conference on Systems, Signal and Image Processing, pp. 1–4 (2011)

    Google Scholar 

  24. Ren, J.M., Chen, Z.S., Jang, J.S.R.: On the use of sequential patterns mining as temporal features for music genre classification. In: IEEE Conference on Acoustics, Speech and Signal Processing, pp. 2294–2297 (2010)

    Google Scholar 

  25. Peeters, G.: A large set of audio features for sound description (similarity and classification). CUIDADO project, CUIDADO I.S.T. Project Report (2004)

    Google Scholar 

  26. West, K., Cox, S.: Finding an optimal segmentation for audio genre classification. In: Proceedings of the 6th International Symposium on Music Information Retrieval, London, UK (2005)

    Google Scholar 

  27. Müller, M., Ewert, S.: Chroma toolbox: MATLAB implementations for extracting variants of chroma-based audio features. In: Proceedings of the 12th International Conference on Music Information Retrieval (ISMIR), Miami, USA (2011)

    Google Scholar 

  28. Grosche, P., Müller, M.: Tempogram toolbox: MATLAB tempo and pulse analysis of music recordings. In: 12th International Conference on Music Information Retrieval (ISMIR, late-breaking contribution), Miami, USA (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE Department, Institute of Technology and Marine Engineering, Kolkata, West Bengal, India

    Arijit Ghosal

  2. Indian Statistical Institute, Kolkata, India

    Rudrasis Chakraborty

  3. IT Department, Jadavpur University, Kolkata, India

    Bibhas Chandra Dhara

  4. CSE Department, Jadavpur University, Kolkata, India

    Sanjoy Kumar Saha

Authors
  1. Arijit Ghosal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rudrasis Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bibhas Chandra Dhara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanjoy Kumar Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arijit Ghosal .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Orissa, India

    Durga Prasad Mohapatra

  2. Computer Science and Engineering, SOA University, Bhubaneswar, India

    Srikanta Patnaik

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer India

About this paper

Cite this paper

Ghosal, A., Chakraborty, R., Dhara, B.C., Saha, S.K. (2014). Genre-Based Classification of Song Using Perceptual Features. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_26

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1665-0_26

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1664-3

  • Online ISBN: 978-81-322-1665-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation