Fractional Fourier Transform Based Features for Musical Instrument Recognition Using Machine Learning Techniques

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


This paper reports the result of Musical instrument recognition using fractional fourier transform (FRFT) based features. The FRFT features are computed by replacing conventional Fourier transform in Mel Frequecny Cepstral coefficient ( MFCC) with FRFT. The result of the system using FRFT is compared with the result of the system using Mel Frequency Cepstral Coefficients (MFCC), Wavelet and Timbrel features with different machine learning algorithms. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system using FRFT features outperforms over MFCC, Wavelet and Timbrel features with 91.84% recognition accuracy for individual instruments. The system is tested on benchmarked McGill University musical sound database. The experimental result shows that musical sound signals can be better represented using FRFT.


Musical instrument recognition Mel Frequency Cepstral Coefficient (MFCC) Fractional Fourier transform (FRFT) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Eronen, A.: Comparison of features for Musical instrument recognition. In: Proceeding of IEEE Workshop Applications of Signal Processing to Audio and Acoustic, pp. 19–22 (2001)Google Scholar
  2. 2.
    Martin, K.D., Kin: Musical Instrument recognition: A pattern recognition approach. Journal of Acoustical Society of America 109, 1068 (1998)Google Scholar
  3. 3.
    Deng, J.D., Simmermacher, C., Cranefield, S.: A study on Feature analysis for Musical Instrument Classification. IEEE Transaction on Systems, Man and Cybernetics 38(2), 429–438 (2008)CrossRefGoogle Scholar
  4. 4.
    Eronen, A., Klapuri, A.: Musical Instrument Recognition using cepstral coefficients and Temporal features. In: ICASSP (2000)Google Scholar
  5. 5.
    Kaminskyj, I., Czaszejko, T.: Automatic Recognition of Isolated Monophonic Musical Instrument Sounds using k-NNC. Journal of Intelligent Information Systems 24(2/3), 199–221 (2005)CrossRefGoogle Scholar
  6. 6.
    Agostini, G., Longari, M., Pollastri, E.: Content-Based Classification of Musical Instrument Timbres. IEEE Signal Processing Society (2003)Google Scholar
  7. 7.
    Essid, Richard, David: Hierarchical Classification of Musical Instruments on Solo Recordings. In: Proceedings of International Conference (2006)Google Scholar
  8. 8.
    Narayan, V.A., Prabhu, K.M.M.: The fractional Fourier transform: Theory, implementation and error analysis. Int. Journal of Microprocessors and Microsystems 27(10), 511–521Google Scholar
  9. 9.
    Ozaktas, H.M., Zalevsky, Z., Kutay, M.A.: The fractional Fourier transform with applications in optics and signal processing. Wiley, New York (2001)Google Scholar
  10. 10.
    Namias, V.: The fractional order Fourier transform and its application to quantum mechanics. IMA Journal of Appl. Math. 25(3), 241–265 (1980)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
  12. 12.
    Agostini, G., Longari, M., Poolastri, E.: Musical instrument timbres classification with spectral features. EURASIP J. Appl. Signal Process. (1), 5–14 (2003), doi:10.1155/ S1110865703210118Google Scholar
  13. 13.
    Kostek, B.: Musical instrument classification and duet analysis employing music information retrieval techniques. Proc. IEEE 92(4), 712–729 (2004)CrossRefGoogle Scholar
  14. 14.
    Ajmera, P.K., Holambe, R.S.: Fractional Fourier transform based features for speaker recognition using support vector machine. Int. Journal of Computer and Electrical Engineering (2012)Google Scholar
  15. 15.
    Witten, H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)Google Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Springer, San Mateo, Appendix (1993)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • D. G. Bhalke
    • 1
  • C. B. Rama Rao
    • 1
  • D. S. Bormane
    • 2
  1. 1.NITWarangalIndia
  2. 2.JSPM’s RSCOEPuneIndia

Personalised recommendations