Advertisement

A Novel Approach to String Instrument Recognition

  • Anushka Banerjee
  • Alekhya Ghosh
  • Sarbani PalitEmail author
  • Miguel Angel Ferrer Ballester
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)

Abstract

In music information retrieval, identifying instruments has always been a challenging aspect for researchers. The proposed approach offers a simple and novel approach with highly accurate results in identifying instruments belonging to the same class, the string family in particular. The method aims to achieve this objective in an efficient manner, without the inclusion of any complex computations. The feature set developed using frequency and wavelet domain analyses has been employed using different prevalent classification algorithms ranging from the primitive k-NN to the recent Random Forest method. The results are extremely encouraging in all the cases. The best results include achieving an accuracy of 89.85% by SVM and 100% accuracy by Random Forest method for four and three instruments respectively. The major contribution of this work is the achievement of a very high level of accuracy of identification from among the same class of instruments, which has not been reported in existing works. Other significant contributions include the construction of only six features which is a major factor in bringing down the data requirements. The ultimate benefit is a substantial reduction of computational complexity as compared to existing approaches.

Keywords

Music information retrieval Harmonic components Wavelet coefficients SVM Random Forest 

References

  1. 1.
    De Poli, G., Prandoni, P.: Sonological models for timbre characterization. J. New Music Res. 26(1997), 170–197 (1997)CrossRefGoogle Scholar
  2. 2.
    Kaminsky, I., Materka, A.: Automatic source identification of monophonic musical instrument sounds. In: Proceedings of the 1995 IEEE International Conference of Neural Networks, pp. 189–194 (1995)Google Scholar
  3. 3.
    Eronen, A., Klapuri, A.: Musical instrument recognition using cepstral coefficients and temporal features. In: Proceedings of 2000 IEEE International Conference on Acoustics, Speech Signal Processing, vol. 2, pp. II753–II756 (2000)Google Scholar
  4. 4.
    Eronen, A.: Comparison of features for musical instrument recognition. In: 2001 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 19–22 (2001)Google Scholar
  5. 5.
    Krishna, A.G., Sreenivas, T.V.: Music instrument recognition: from isolated notes to solo phrases. In: Proceedings of IEEE International Conference on ASSP, vol. 4, pp. iv-265–iv-268 (2004)Google Scholar
  6. 6.
    Heittola, T., Klapuri, A., Virtanen, T.: Musical instrument recognition in polyphonic audio using source-filter model for sound separation. In: Proceedings of International Society for Music Information Retrieval Conference, pp. 327–332 (2009)Google Scholar
  7. 7.
    Duan, Z., Pardo, B., Daudet, L.: A novel cepstral representation for timbre modeling of sound sources in polyphonic mixtures. In: Proceedings of 2014 IEEE International Conference on ASSP, pp. 7495–7499 (2014)Google Scholar
  8. 8.
    Han, Y., Kim, J., Lee, K.: Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Trans. Audio Speech Lang. Process. 25(1), 208–221 (2017)CrossRefGoogle Scholar
  9. 9.
    Foomany, F.H., Umapathy, K.: Classification of music instruments using wavelet-based time-scale features. In: IEEE International Conference on Multimedia & Expo Workshops (2013)Google Scholar
  10. 10.
    Kothe, R.S., Bhalke, D.G.: Musical instrument recognition using wavelet coefficient histograms. In: Proceedings of Emerging Trends in Electronics and Telecommunication Engineering (NCET 2013), pp. 37–41 (2013)Google Scholar
  11. 11.
    Institute for Research and Coordination in Acoustics/Music (IRCAM), Pompidou, FranceGoogle Scholar
  12. 12.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (1967)CrossRefGoogle Scholar
  13. 13.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 532 (2001)CrossRefGoogle Scholar
  14. 14.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anushka Banerjee
    • 1
  • Alekhya Ghosh
    • 2
  • Sarbani Palit
    • 3
    Email author
  • Miguel Angel Ferrer Ballester
    • 4
  1. 1.Maulana Abul Kalam Azad University of TechnologyKolkataIndia
  2. 2.Institute of Radio Physics and ElectronicsUniversity of CalcuttaKolkataIndia
  3. 3.Indian Statistical InstituteKolkataIndia
  4. 4.Universidad de Las Palmas de Gran CanariaLas PalmasSpain

Personalised recommendations