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Stratification of String Instruments Using Chroma-Based Features

  • Arijit GhosalEmail author
  • Suchibrota DuttaEmail author
  • Debanjan Banerjee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)

Abstract

Identification of instrument type from acoustic signal is a challenging issue. It is also an interesting and popular research area having several promising applications in music industry. Researchers have already been able to classify instruments into several broad categories like String, Woodwind, Percussion, and Keyboard etc. using acoustic features like Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR) etc. MFCC has been found to be excessively used. In this work an alternative acoustic feature of MFCC has been proposed. Chroma is an octave independent estimation of strength of all possible notes in Western 12 note scale at different points of time. Sound envelope originated by a note reflects the signature of an instrument and this can be used to stratify String instruments into various categories. The proposed work relies on Chroma-based low-dimensional feature vector to categorize String instruments. For classification purpose, simple and popular classifiers like Neural Network, k-NN, Naïve Bayes’ have been exercised.

Keywords

Instrument classification Chroma Note Neural network Classification 

References

  1. 1.
  2. 2.
    Agostini, G., Longari, M., Pollastri, E.: Musical instrument timbres classification with spectral features. EURASIP J. Appl. Signal Process. 2003, 5–14 (2003)Google Scholar
  3. 3.
    Zhu, J., Xue, X., Lu, H.: Musical genre classification by instrumental features. ICMC (2004)Google Scholar
  4. 4.
    Essid, S., Richard, G., David, B.: Hierarchical classification of musical instruments on solo recordings. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol. 5 (2006)Google Scholar
  5. 5.
    Sinith, M.S., Rajeev, K.: Pattern recognition in South Indian classical music using a hybrid of HMM and DTW. In: IEEE International Conference on Computational Intelligence and Multimedia Applications, vol. 2 (2007)Google Scholar
  6. 6.
    Deng, J.D., Simmermacher, C., Cranefield, S.: A study on feature analysis for musical instrument classification. IEEE Trans. Syst. Man Cybern. Part B 38(2), 429–438 (2008)Google Scholar
  7. 7.
    Gunasekaran, S., Revathy, K.: Recognition of Indian musical instruments with multi-classifier fusion. In: IEEE International Conference on Computer and Electrical Engineering (ICCEE 2008), pp. 847–851 (2008)Google Scholar
  8. 8.
    Senan, N. et al.: Feature extraction for traditional malay musical instruments classification system. In: IEEE International Conference on Soft Computing and Pattern Recognition (SOCPAR, 2009) pp. 454–459 (2009)Google Scholar
  9. 9.
    Liu, J., Xie, L.: SVM-based automatic classification of musical instruments. In: International Conference on Intelligent Computation Technology and Automation (ICICTA, 2010), vol. 3, pp. 669–673 (2010)Google Scholar
  10. 10.
    Kumari, M., Kumar, P., Solanki, S.S.: In: Classification of North Indian Musical Instruments using Spectral Features, GESJ: Computer Science and Telecommunication, vol. 6, pp. 11–24 (2010)Google Scholar
  11. 11.
    Barbedo, J.G.A., Tzanetakis, G.: Instrument identification in polyphonic music signals based on individual partials. In: IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP, 2010), pp. 401–404 (2010)Google Scholar
  12. 12.
    Barbedo, J.G.A., Tzanetakis, G.: Musical instrument classification using individual partials. IEEE Trans. Audio Speech Lang. Process. 19(1), 111–122 (2011)CrossRefGoogle Scholar
  13. 13.
    Ghosal, A., Chakraborty, R., Dhara, B.C., Saha, S.K.: Automatic identification of instrument type in music signal using wavelet and MFCC. In: Venugopal, K.R., Patnaik, L.M. (eds.) Computer Networks and Intelligent Computing. Communications in Computer and Information Science, vol. 157, pp. 560–565. Springer, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Grindlay, G., Ellis, D.P.W.: Transcribing multi-instrument polyphonic music with hierarchical Eigen instruments. IEEE J. Sel. Top. Signal Process. 5(6), 1159–1169 (2011)CrossRefGoogle Scholar
  15. 15.
    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). hal-00727791, version 2–22 (2011)Google Scholar
  16. 16.
    Chandwadkar, D.M., Sutaone, M.S.: Role of features and classifiers on accuracy of identification of musical instruments. In: IEEE 2nd National Conference on Computational Intelligence and Signal Processing (CISP, 2012), pp. 66–70 (2012)Google Scholar
  17. 17.
    Joshi, M., Nadgir, S.: Extraction of feature vectors for analysis of musical instruments. In: IEEE International Conference on Advances in Electronics, Computers and Communications (ICAECC, 2014), pp. 1–6 (2014)Google Scholar
  18. 18.
    Gaikwad, S., Chitre, A.V., Dandawate, Y.H.: Classification of Indian classical instruments using spectral and principal component analysis based cepstrum features. In: IEEE International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC, 2014), pp. 276–279 (2014)Google Scholar
  19. 19.
    Arora, V., Behera, L.: Instrument identification using PLCA over stretched manifolds. In: IEEE Twentieth National Conference on Communications (NCC), pp. 1–5 (2014)Google Scholar
  20. 20.
    Dandawate, Y.H., Kumari, P., Bidkar, A.: Indian instrumental music: Raga analysis and classification. In: IEEE 1st International Conference on Next Generation Computing Technologies (NGCT, 2015), pp. 725–729 (2015)Google Scholar
  21. 21.
    Abeber, J., Weib, C.: Automatic recognition of instrument families in polyphonic recordings of classical music. In: 16th International Society for Music Information Retrieval Conference (2015)Google Scholar
  22. 22.
    Kumar, P.A.M., Sebastian, J., Murthy, H.A.: Musical onset detection on carnatic percussion instruments. In: IEEE Twenty First National Conference on Communications (NCC, 2015), pp. 1–6 (2015)Google Scholar
  23. 23.
    Bhalke, D.G., Rao, C.B.R., Bormane, D.S.: Automatic musical instrument classification using fractional Fourier transform based-MFCC features and counter propagation neural network. J. Intell. Inf. Syst. 46(3), 425–446 (2016)CrossRefGoogle Scholar
  24. 24.
    Ghisingh, S., Mittal, V.K.: Classifying musical instruments using speech signal processing methods. In: IEEE Annual India Conference (INDICON, 2016), pp. 1–6 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information TechnologySt. Thomas’ College of Engineering & TechnologyKolkataIndia
  2. 2.Department of Information Technology and MathematicsRoyal Thimphu CollegeThimphuBhutan
  3. 3.Department of Management Information SystemsSarva Siksha MissionKolkataIndia

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