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Music Genre Classification using MFCC, SVM and BPNN

by Gursimran Kour, Neha Mehan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 6
Year of Publication: 2015
Authors: Gursimran Kour, Neha Mehan
10.5120/19669-1119

Gursimran Kour, Neha Mehan . Music Genre Classification using MFCC, SVM and BPNN. International Journal of Computer Applications. 112, 6 ( February 2015), 12-14. DOI=10.5120/19669-1119

@article{ 10.5120/19669-1119,
author = { Gursimran Kour, Neha Mehan },
title = { Music Genre Classification using MFCC, SVM and BPNN },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 6 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 12-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number6/19669-1119/ },
doi = { 10.5120/19669-1119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:48:43.386410+05:30
%A Gursimran Kour
%A Neha Mehan
%T Music Genre Classification using MFCC, SVM and BPNN
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 6
%P 12-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the field of musical information retrieval, genre categorization is a complicated mission. MFCC is one of the feature extraction method use in classification of musical genre that is based on short speech signals. Searching and organizing are the main characteristics of the music genre classification system these days. This paper describes a new technique that uses support vector machines to classify songs based on features using MFCC, BPNN and SVM classifier does not classify songs based on the short signals. So these categories a number of acoustic features that include Mel-frequency Cepstral coefficients are extracted to characterize the audio content. Support vector machines and BPNN classifies audio into their respective classes by learning from training data. The simulation is taken place in MATLAB by making experiments on different genres . The results obtained by this proposed technique are promising.

References
  1. Abu-El-Quran, A. R. , Goubran, R. A. , & Chan, A. D. C. (2006). Security monitoring using microphone arrays and audio classification. IEEE Transactions on Instrumentation and Measurement, 55(4), 1025–1032.
  2. Ajmera, J. , McCowan, I. , & Bourlard, H. (2003). Speech/music segmentation using entropy and dynamism features in a HMM classification framework. Speech Communication, 40(3), 351–363.
  3. Duda, R. O. , Hart, P. E. , & Stork, D. G. (2001). Pattern classification. New York: John Wiley-Interscience. Eronen, A. J. , Peltonen, V. T. , Tuomi, J. T. , Klapuri, A. P. , Fagerlund, S.
  4. Sorsa, T. , et al. (2006). Audio-based context recognition. IEEE Transactions on Audio, Speech and Language Processing, 14(1), 321–329.
  5. Esmaili, S. , Krishnan, S. , & Raahemifar, K. (2004). Content based audio classification and retrieval using joint time–frequency analysis. In IEEE international conference on acoustics, speech and signal processing (pp. 665–668).
  6. Guo, G. , & Li, S. Z. (2003). Content-based audio classification and retrieval by support vector machines. IEEE Transactions on Neural Networks, 14(1), 308–315.
  7. Haykin, S. (2001). Neural networks a comprehensive foundation. Asia: Pearson Education.
  8. Huang, R. , & Hansen, J. H. L. (2006). Advances in unsupervised audio classification and segmentation for the broadcast news and NGSW corpora. IEEE Transactions on Audio, Speech and Language Processing, 14(3), 907–919.
  9. Jiang, H. , Bai, J. , Zhang, S. , & Xu, B. (2005). SVM-based audio scene classification. Proceeding of the IEEE, 131–136.
  10. Kiranyaz, S. , Qureshi, A. F. , & Gabbouj, M. (2006). A generic audio classification and segmentation approach for multimedia indexing and retrieval. IEEE Transactions on Audio, Speech and Language Processing, 14(3), 1062–1081.
  11. Li, S. Z. (2000). Content-based audio classification and retrieval using the nearest feature line method. IEEE Transactions on Speech and Audio Processing, 8(5), 619–625.
  12. Lin, C. -C. , Chen, S. -H. , Truong, T. -K. , & Chang, Y. (2005). Audio classification and categorization based on wavelets and support vector machine. IEEE Transactions on Speech and Audio Processing, 13(5), 644–651.
  13. Li, D. , Sethi, I. K. , Dimitrova, N. , & McGee, T. (2001). Classification of general audio data for content-based retrieval. Pattern Recognition Letters, 22, 533–544.
  14. Lu, L. , Zhang, H. -J. , & Li, S. Z. (2003). Content-based audio classification and segmentation by using support vector machines. Multimedia Systems, 8, 482–492.
  15. McConaghy, T. , Leung, H. , Boss, E. , & Varadan, V. (2003). Classification of audio radar signals using radial basis function neural networks. IEEE Transactions on Instrumentation and Measurement, 52(6), 1771–1779.
  16. Mubarak, O. M. , Ambikairajah, E. , & Epps, J. (2005). Analysis of an MFCC-based audio indexing system for efficient coding of multimedia sources. In IEEE international conference on acoustics, speech and signal processing (pp. 619–622).
  17. Palanivel, S. (2004). Person authentication using speech, face and visual speech. Ph. D thesis, Madras: IIT.
  18. Panagiotakis, C. , & Tziritas, G. (2005). A speech/music discriminator based on rms and zero-crossings. IEEE Transactions on Multimedia, 7(1), 155–156.
  19. Rabiner, L. , & Juang, B. (2003). Fundamentals of speech recognition. Singapore: Pearson Education.
Index Terms

Computer Science
Information Sciences

Keywords

SVM MFCC BPNN training classification feature extraction.

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