Multimedia Tools and Applications

, Volume 76, Issue 20, pp 20719–20737 | Cite as

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

  • Yina GuoEmail author
  • Qijia Liu
  • Anhong Wang
  • Chaoli Sun
  • Wenyan Tian
  • Ganesh R. Naik
  • Ajith Abraham


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 %.


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



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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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Taiyuan University of Science and TechnologyTaiyuanChina
  2. 2.Centre for Health Technologies (CHT)University of TechnologySydneyAustralia
  3. 3.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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