Skip to main content
SpringerLink
Log in

Discrete wavelet packet transform and ensembles of lazy and eager learners for music genre classification

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

This paper presents a process for determining the music genre of an item using a new set of descriptors. A discrete wavelet packet transform is applied to obtain the signal representation at two different resolutions: a frequency resolution and a time resolution tuned to encode music notes and their onset and offset. These features are tested on a number of data sets as descriptors for music genre classification. Lazy learning classifiers (k-nearest neighbor) and eager learners (neural networks and support vector machines) are applied in order to assess the classification power of the proposed features. Different feature selection techniques and ensemble methods are explored to maximize the accuracy of the classifiers and stabilize their behavior. Our evaluation shows that these frequency descriptors perform better than a standard approach based on Mel-Frequency Cepstral Coefficients and on the Short Time Fourier Transform in music genre classification. Moreover, our work confirms that a parameterization of the music rhythm based on the beat-histogram provides some meaningful information in the context of music classification by genre.Finally, our evaluation suggests that multi-class support vector machines with a linear kernel and round-robin binarization are the simplest and more effective process for music genre classification.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Allamanche, E., Herre, J., Hellmuth, O., Froeba, B., Kastner, T., Kremer, M.: Content-based identification of audio material using mpeg-7 low level description. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2001). Bloomington, IN, USA (2001)

  2. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm (2001)

  3. Cunningham, P., Carney, J.: Diversity versus quality in classification ensembles based on feature selection. In: Eleventh European Conference on Machine Learning (ECML 2000). pp. 109–116 Springer-Verlag, Berlin (2000)

    Google Scholar 

  4. Daubechies, I.: Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia, PA, USA (1992)

  5. Dietterich, T.G.: Ensemble methods in machine learning. Lect. Notes Comput. Sci. 1857, 1–15 (2000)

    Article  Google Scholar 

  6. Dixon, E.P., Widmer, G.: Classification of dance music by periodicity patterns. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2003). Baltimore, MA, USA (2003)

  7. Fausett, L.: Fundamentals of Neural Networks: Architecture, Algorithms and Applications. Prentice-Hall, Englewood Cliffs (1994)

    Google Scholar 

  8. Foote, T.: Content-based retrieval of music and audio. In: Multimedia Storage and Archiving Systems II, Proceedings of the SPIE, vol. 3229, pp. 138–147 (1997)

  9. Foote, J.: Arthur: retrieving orchestral music by long term structure. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2000). Plymouth, MA, USA, (2000)

  10. Fürnkranz, J.: Round robin rule learning. In: ICML '01: Proceedings of the Eighteenth International Conference on Machine Learning. pp. 146–153. San Francisco, CA, USA, Morgan Kaufmann (2001)

  11. Grimaldi, M., Cunningham, P., Kokaram, A.: A wavelet packet representation of audio signals for music genre classification using different ensemble and feature selection. In: Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval. Berkeley, CA, USA (2003)

  12. Grimaldi, M.: Learning to Annotate Music Files using Content Based Retrieval Systems and Wavelet Packet Approximations of the Input Signals. PhD thesis, Trinity College Dublin (2004)

  13. Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Trans. Neural Networks 13(2), 415–425 (2002)

    Article  Google Scholar 

  14. Kohavi, R., John, G.H.: Wrappers for feature subset selection. IEEE Trans. Neural Networks 97(1-2), 273–324 (1997)

    MATH  Google Scholar 

  15. Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance, New Orleans, USA, AAAI Press (1994)

  16. Li, T., Ogihara, M., Li, Q.: A comparative study on content-based music genre classification. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 282–289 (2003)

  17. Logan, B.: Mel frequency cepstral coefficients for music modeling. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2000). Plymouth, MA, USA, 2000

  18. Mallat, S.G.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1999)

    MATH  Google Scholar 

  19. Martin, K.: Musical instrument identification: a pattern-recognition approach. In: The 136th Meeting of the Acoustical Society of America (1998)

  20. Martin, K.D.: Toward automatic sound source recognition: identifying musical instruments. In: Proceedings of the NATO Computational Hearing Advanced Institute. Il Ciocco, Italy (1998)

  21. McKinney, M.F., Breebaart, J.: Features for audio and music classification. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2003). Baltimore, MA, USA (2003)

  22. Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  23. Pienimäki, A.: Indexing music databases using automatic extraction of frequent phrases. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR). Paris, France (2002)

  24. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  25. Scheirer, E.D.: Tempo and beat analysis of acoustic musical signals. J. Acouststic Soc. Am. 103(1), 588–601 (1998)

    Article  Google Scholar 

  26. Scheirer, E., Slaney, M.: Construction and evaluation of arobust multifeatures speech/music discriminator. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), vol. 2, pp. 1331–1334. Munich, Germany

  27. Tzanetakis, G., Ermolinskyi, A., Cook, P.: Pitch histograms in audio and symbolic music information retrieval. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR 2002). Paris, France (2002)

  28. Tzanetakis, G., Essl, G., Cook, P.: Automatic musical genre classification of audio signals. In: Proceedings of the International Symposium on Music Information Retrieval (ISMIR). Bloomington, IN, USA (2001)

  29. Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  30. Wang, Y., Liu, Z., Huang, J.C.: Multimedia content analysis using both audio and visual clues. IEEE Signal Process. Magazine 12–36 (2000)

  31. Zenobi, G., Cunningham, P.: Using diversity in preparing ensemble of classifiers based on different subsets to minimize generalization error. In: 12th European Conference on Machine Leaming (ECML 2001). Springer-Verlag, Berlin (2001)

    Google Scholar 

  32. Zhang, T., Kuo, C.C.: Hierarchical classification of audio data for archiving and retrieving. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP). Phoenix, AZ, USA (1999)

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University College Dublin, Dublin, Ireland

    Marco Grimaldi

  2. Computer Science Department, Trinity College Dublin, Dublin, Ireland

    P´draig Cunningham

  3. Electronic and Electrical Engineering Department, Trinity College Dublin, Dublin, Ireland

    Anil Kokaram

Authors
  1. Marco Grimaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P´draig Cunningham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anil Kokaram
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Grimaldi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Grimaldi, M., Cunningham, P. & Kokaram, A. Discrete wavelet packet transform and ensembles of lazy and eager learners for music genre classification. Multimedia Systems 11, 422–437 (2006). https://doi.org/10.1007/s00530-006-0027-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-006-0027-z

Keywords

Search

Navigation