SVM Based Classification of Sounds from Musical Instruments using MFCC Features
Main Article Content
Abstract
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
References
P. Herrera, G. Peeters and S. Dubnov, “Automatic Classification of Musical Instrument Soundsâ€, Journal of New Music Research, Vol. 32, 2003.
M. J. Newton and L. S. Smith, “A neurally inspired musical instrument classification system based upon the sound onsetâ€, The Journal of Acoustic Society of America, 131(6):4785-98. doi: 10.1121/1.4707535, June 2012.
A. Livshin, G. Peeters and X. Rodet, “Studies and Improvements in Automatic Classification of Musical Sound Samplesâ€, ICMC 2003, Singapore. pp.1-1,October 2003.
C. M. Bishop, “Pattern Recognition and Machine Learningâ€, Springer, 2007.
R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classificationâ€, (2nd Ed). John Wiley & Sons, 2000.
K. Kido, “In Digital Fourier Analysis: Advanced Techniquesâ€, Springer, 2014.
Klautau and Aldebaro, “The MFCCâ€, In Technical report, Signal Processing Lab, UFPA, Brasil, 2005.
R. C. Gonzalez and R. E. Woods, “Digital Image Processingâ€, (3rd. ed): Pearson Education, 2009.
F. Zheng, G. Zhang and Z. Song, “Comparison of Different Implementations of MFCCâ€, In Journal of Computer Science & Technology, vol. 16: 582–589, 2001.
B. Logan, “Mel Frequency Cepstral Coefficients for Music modelingâ€, In International Symposium on Music Information Retrieval, 2000.
S. Muhury, G. Neogi, P. Debnath and J. Ghosh Dastidar, “Design of a voice-based system by recognizing speech using MFCCâ€, Computational Science and Engineering Proceedings of the International Conference on Computational Science and Engineering (ICCSE2016), CRC Press , pp 77–80, DOI: 10.1201/9781315375021-16, October 2016.
D. O’Shaughnessy, “Pattern Recognitionâ€, Volume 41 Issue 10: 2965-2979, 2008.