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Automatic musical instrument classification using fractional fourier transform based- MFCC features and counter propagation neural network

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Abstract

This paper presents a novel feature extraction scheme for automatic classification of musical instruments using Fractional Fourier Transform (FrFT)-based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using Counter Propagation Neural Network (CPNN). The discriminating capability of the proposed features have been maximized for between-class instruments and minimized for within-class instruments compared to other conventional features. Also, the proposed features show significant improvement in classification accuracy and robustness against Additive White Gaussian Noise (AWGN) compared to other conventional features. McGill University Master Sample (MUMS) sound database has been used to test the performance of the system.

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Acknowledgments

The authors are very much thankful to the anonymous reviewers for their constructive comments and valuable suggestions to improve quality of this manuscript significantly.

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Authors and Affiliations

  1. National Institute of Technology, Warangal, India

    D. G. Bhalke & C. B. Rama Rao

  2. Rajarshi Shahu College of Engineering, Tathawade, Pune, India

    D. S. Bormane

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  1. D. G. Bhalke
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  2. C. B. Rama Rao
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  3. D. S. Bormane
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Correspondence to D. G. Bhalke.

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Bhalke, D.G., Rao, C.B.R. & Bormane, D.S. Automatic musical instrument classification using fractional fourier transform based- MFCC features and counter propagation neural network. J Intell Inf Syst 46, 425–446 (2016). https://doi.org/10.1007/s10844-015-0360-9

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  • DOI: https://doi.org/10.1007/s10844-015-0360-9

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