Indian Musical Instrument Recognition Using Gaussian Mixture Model

  • Swarupa R. Patil
  • Sheetal J. Machale
Conference paper


For musical data analysis and retrieval, instrument recognition plays an important role. It helps in music search by instruments, makes music transcription easier and more accurate. There is need of extensive study on Indian musical instruments for various applications. In this paper, a system has been described for musical instrument recognition in monophonic audio signals where the single sound source is active at a time. The proposed system uses samples from three Indian classical instruments i.e, flute, harmonium, and sitar. The extracted feature set includes statistical, temporal and spectral, cepstral and linear predictive features. For the classification task, experimental results have been provided using a Gaussian mixture model (GMM). The system has shown recognition accuracy of 93.18% (average) for a combination of MFCC as a feature and GMM as a classifier.


Musical signal processing Musical instrument recognition Temporal and spectral features Mel frequency cepstral coefficients (MFCC) Linear predictive coefficients (LPC) Gaussian mixture model (GMM) 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Swarupa R. Patil
    • 1
  • Sheetal J. Machale
    • 1
  1. 1.SVERI’s College of EngineeringPandharpurIndia

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