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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(2): 172-175 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.296

Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments

D. M. Chandwadkar and M. S. Sutaone
Abstract—Selection of effective feature set and proper classifier is a challenging task in problems where machine learning techniques are used. In automatic identification of musical instruments also it is very crucial to find the right set of features and accurate classifier. In this paper, the role of various features with different classifiers on automatic identification of musical instruments is discussed. Piano, acoustic guitar, xylophone and violin are identified using various features and classifiers. Spectral features like spectral centroid, spectral slope, spectral spread, spectral kurtosis, spectral skewness and spectral roll-off are used along with autocorrelation coefficients and Mel Frequency Cepstral Coefficients (MFCC) for this purpose. The dependence of instrument identification accuracy on these features is studied for different classifiers. Decision trees, k nearest neighbour classifier, multilayer perceptron, Sequential Minimal Optimization Algorithm (SMO) and multi class classifier (metaclassifier) are used. It is observed that accuracy can be improved by proper selection of these features and classifier.

Index Terms—Feature extraction, classification, musical instrument identification.

D. M. Chandwadkar is with Department of Electronics and Telecommunication, K. K. Wagh Institute of Engineering Education & Research, Nashik, Maharashtra, India. (e-mail: dmc.eltx@kkwieer.org).
Dr. M. S. Sutaone is with the Department of Electronics and Telecommunication, Government College of Engineering, Pune, Maharashtra, India. (e-mail: mssutaone.extc@coep.ac.in).

[PDF]

Cite:D. M. Chandwadkar and M. S. Sutaone, "Selecting Proper Features and Classifiers for Accurate Identification of Musical Instruments," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 172-175, 2013.

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