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Identification of Musical Instruments Using MFCC Features

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • Sushen R. Gulhane

    (DYPCOE (SPPU), DYPIT)

  • D. Shirbahadurkar Suresh

    (Zeel COE (SPPU), DYPIT)

  • S. Badhe Sanjay

    (DYPCOE (SPPU), DYPIT)

Abstract

The general aim of our study and this research is to find out better classifier for musical device identification with great accuracy. This is one of the most popular topics for study. In our research paper, we present the idea to identify the musical instrument from a monophonic audio signal. For this purpose, we have used Cepstral features (i.e. MFCC features) extraction technique for extraction of features and there is the number of classifiers out of which, we have used SVM and KNN classifiers for sorting purpose. We have compared the results from both classifiers. In our work, we have made a catalog of different music samples from various musical instruments. We use this catalog for both training and testing purpose.

Suggested Citation

  • Sushen R. Gulhane & D. Shirbahadurkar Suresh & S. Badhe Sanjay, 2020. "Identification of Musical Instruments Using MFCC Features," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 957-968, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_97
    DOI: 10.1007/978-3-030-41862-5_97
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