Identification of Musical Instruments Using MFCC Features

Sushen R. Gulhane, D. Shirbahadurkar Suresh and S. Badhe Sanjay
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Sushen R. Gulhane: DYPCOE (SPPU), DYPIT
D. Shirbahadurkar Suresh: Zeel COE (SPPU), DYPIT
S. Badhe Sanjay: DYPCOE (SPPU), DYPIT

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 957-968 from Springer

Abstract: 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.

Keywords: Musical instrument identification; Music signal; Monophonic; Feature extraction; Classifier (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_97

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DOI: 10.1007/978-3-030-41862-5_97

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