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Optimized phase-space reconstruction for accurate musical-instrument signal classification

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Traditional musical-instrument classification methods mainly use regions in the time or/and frequency characteristics, cepstrum characteristics, and MPEG-7 characteristics, and they often lead to erroneous classification. Therefore, there is need to develop a more suitable method that is more applicable to the nonlinear characteristics of musical-instrument signals and can avoid the abovementioned problems. In this paper, a musical-instrument classification method that couples the optimized phase-space reconstruction (OPSR) with a flexible neural tree (FNT) is proposed. As per nonlinear dynamic theory, a principal component analysis and correlation coefficient are used to optimize the phase-space reconstruction (PSR) method. Multidimensional PSR results for different musical-instrument signals are extracted as the main components, and the dimensionality is reduced by the OPSR method. A probability density function (PDF) is introduced in the feature extraction step to differentiate musical instruments according to the phase-space-reconstructible characteristics. A FNT is adopted as a classifier to tackle the variability in musical-instrument signals and to improve the adaptive ability of various target classification problems. Experimental testing has been conducted to show that the proposed OPSR–PDF–FNT algorithm gives superior performance over other comparable algorithms and can classify 12 musical instruments with an accuracy of 98.2 %.

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  1. 1.

    Agostini G (2003) Musical instrument timbres classification with spectral features. EURASIP J Appl Signal Process 1:5–14

    Article  Google Scholar 

  2. 2.

    Bao W, Chen Y, Wang D (2014) Prediction of protein structure classes with flexible neural tree. Bio-Med Mater Eng 24(6):3797–3806

    Google Scholar 

  3. 3.

    Barbedo JGA, Tzanetakis G (2011) Musical instrument classification using individual partials. IEEE Trans Audio Speech Lang Process 19(1):111–122

    Article  Google Scholar 

  4. 4.

    Benetos E, Kotti M, Kotropoulos C (2007) Large scale musical instrument identification. Paper presented at the 4th Sound and Music Computing Conference 11: 13

  5. 5.

    Bhalke DG, Rao CBR, Bormane DS (2014) Musical instrument classification using higher order spectra. Signal Processing and Integrated Networks (SPIN), 2014 International Conference on. IEEE 40–45

  6. 6.

    Bouaziz S, Dhahri H, Alimi AM et al (2013) A hybrid learning algorithm for evolving flexible beta basis function neural tree model. Neurocomputing 117:107–117

    Article  Google Scholar 

  7. 7.

    Chen Y, Abraham A (2009) Tree-structure based hybrid computational intelligence: Theoretical foundations and applications. Springer Science & Business Media 2:39–96

  8. 8.

    Chen Y, Abraham A, Yang B (2006) Feature selection and classification using flexible neural tree. Neurocomputing 70(1):305–313

    Article  Google Scholar 

  9. 9.

    Deng JD, Simmermacher C, Cranefield S (2008) A study on feature analysis for musical instrument classification. IEEE Trans Syst Man Cybern B Cybern 38(2):429–438

    Article  Google Scholar 

  10. 10.

    Eronen A (2001) Comparison of features for musical instrument recognition. Applications of Signal Processing to Audio and Acoustics, 2001 I.E. Workshop on the. IEEE 19–22

  11. 11.

    Guo Y, Huang S, Li Y (2012) Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis. Circuits, Systems, and Signal Processing 31(6):2047–2060

    MathSciNet  Article  Google Scholar 

  12. 12.

    Guo Y, Wang Q, Huang S, Abraham A (2012) Flexible neural trees for online hand gesture recognition using surface electromyography. J Comput 7(5):1099–1103

    Article  Google Scholar 

  13. 13.

    Guo Y, Huang S, Li Y, Ganesh RN (2013) Edge effect elimination in single-mixture blind source separation. Circuits, Systems, and Signal Processing 32(5):2317–2334

    MathSciNet  Article  Google Scholar 

  14. 14.

    Guo Y, Wang Q, Huang S, Abraham A (2014) Hand gesture recognition system using single-mixture source separation and flexible neural trees. J Vib Control 20(9):1333–1342

    Article  Google Scholar 

  15. 15.

    Hess S, Kitaura FS (2016) Cosmic flows and the expansion of the local universe from non-linear phase-space reconstructions. Mon Not R Astron Soc 456(4):4247–4255

    Article  Google Scholar 

  16. 16.

    Hong M, Wang D, Wang Y et al (2016) Mid- and long-term runoff predictions by an improved phase-space reconstruction model. Environ Res 148:560–573

    Article  Google Scholar 

  17. 17.

    Joder C, Essid S, Richard G (2009) Temporal integration for audio classification with application to musical instrument classification. IEEE Trans Audio Speech Lang Process 17(1):174–186

    Article  Google Scholar 

  18. 18.

    Jolliffe I (2002) Principal component analysis. John Wiley & Sons, Ltd

  19. 19.

    Kazi FI, Bhalke DG (2014) Musical instrument classification using higher order spectra and hierarchical taxonomies. Int J Comput Appl 107(17):17–22

    Google Scholar 

  20. 20.

    Koulaouzidis G, Das S, Cappiello G et al (2015) Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG. Int J Cardiol 182:38–43

    Article  Google Scholar 

  21. 21.

    McKay C (2010) Automatic music classification with jMIR. Doctoral dissertation, McGill University

  22. 22.

    Misron MM, Rosli N, Manaf NA, Halim HA (2014) Music emotion classification (mec): Exploiting vocal and instrumental sound features. Recent Advances on Soft Computing and Data Mining. Springer International Publishing, pp 539–549

  23. 23.

    Patil K, Elhilali M (2015) Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases. EURASIP JASMP 1:1–13

    Google Scholar 

  24. 24.

    Rui R, Bao C (2012) The musical instrument classification algorithm based on nonlinear dynamics. Acta Electron Sin 7:032

    Google Scholar 

  25. 25.

    Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62(1):77–89

    Article  Google Scholar 

  26. 26.

    Takens F (1981) Detecting strange attractors in turbulence. Dynamical systems and turbulence, Warwick 1980. Springer, Berlin Heidelberg, pp 366–381

    Book  Google Scholar 

  27. 27.

    Wang Y, Wang J, Wei X (2015) A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: a case study of wind farms in northwest China. Energy 91:556–572

    Article  Google Scholar 

  28. 28.

    Xu T, Wang Y, Zhang Z (2013) Pixel-wise skin colour detection based on flexible neural tree. IET Image Process 7(8):751–761

    Article  Google Scholar 

  29. 29.

    Xu B, Jacquir S, Laurent G et al (2014) Analysis of an experimental model of in vitro cardiac tissue using phase space reconstruction. Biomed Signal Process Control 13:313–326

    Article  Google Scholar 

  30. 30.

    Yang B, Chen Y (2016) Somatic mutation detection using ensemble of flexible neural tree model. Neurocomputing 179:161–168

    Article  Google Scholar 

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Funding for this work was supported by the National Natural Science Foundation of China (NO. 61301250, NO. 61401298), Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi (NO. [2015]3), Project of Shanxi Scholarship Council of China (NO. 2014-060), Doctoral Program of Taiyuan University of Science and Technology (NO. 20152003), and Project for “131” Talented Person Project of Higher Learning Institutions of Shanxi (NO. [2016]).

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Correspondence to Yina Guo.

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Guo, Y., Liu, Q., Wang, A. et al. Optimized phase-space reconstruction for accurate musical-instrument signal classification. Multimed Tools Appl 76, 20719–20737 (2017).

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  • Musical-instrument classification
  • Phase-space reconstruction
  • Principal component analysis
  • Flexible neural tree