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  Published Paper Details:

  Paper Title

MUSIC INSTRUMENT SOUND CLASSIFICATION USING KNN

  Authors

  P. Kathirvel,  R. Thiruvengatanadhan,  R. Seenu

  Keywords

Music Instrument Signal, Feature Extraction, Mel-Frequency cepstral coefficients (MFCC), K-Nearest Neighbour (KNN)

  Abstract


Automatic instrument sound classification is very useful in music analysis. This system tested in ten different classes of musical instrument sound from two different instruments. The accuracy of the classification relies on the strength of the features and classification scheme. In this paper, Mel-Frequency cepstral coefficients (MFCC) features are extracted from the input Signal and describes a technique that uses K-Nearest Neighbour (KNN) to classify music. The proposed feature extraction and classification models results in better accuracy in music genre classification.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2112443

  Paper ID - 214265

  Page Number(s) - e237-e239

  Pubished in - Volume 9 | Issue 12 | December 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  P. Kathirvel,  R. Thiruvengatanadhan,  R. Seenu,   "MUSIC INSTRUMENT SOUND CLASSIFICATION USING KNN", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 12, pp.e237-e239, December 2021, Available at :http://www.ijcrt.org/papers/IJCRT2112443.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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