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Evaluation of the Combination of Features for Classifying Traditional Songs

http://hdl.handle.net/20.500.12678/0000007421
7164f764-a2b3-4d68-8153-ded4a440b795
15e76e76-54c6-4e92-ac44-1f7567b63f0e
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Name / File License Actions
Evaluation Evaluation of the Combination of Features for Classifying Traditional Songs.pdf (494 KB)
Publication type
Journal article
Upload type
Publication
Title
Title Evaluation of the Combination of Features for Classifying Traditional Songs
Language en
Publication date 2020-05-12
Authors
May Thu Myint
Phyu Phyu Khaing
Description
In this paper, the problem of music
classification and highly similar of cultural music style
of Myanmar’s ethnic music is examined. The
experiments are conducted by using the combination of
timbre features and combining the nine major features.
For this work, in the use of classification methods,
Sparse Representation Classifier and k-Nearest
Neighbours classifier are commonly used which is to
compare the classification results. Moreover, it shows
that MFCC (FC4, FC5, FC6) feature combination gives
79% of best classification result with the use of SRC
classifier. When all feature combinations are used, the
SRC provide the best classification accuracy of 81.64%
for Shan ethnic songs than other ethnic songs
Keywords
Sparse Representation Classifier, k- Nearest Neighbors, Timbre feature, Mel frequency Cepstral Coefficients
Journal articles
No-1
University Journal of Computer Applications and Research (JCAR 2020)
63-69
Vol-1
0
0
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downloads
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