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DOI: 10.14569/IJACSA.2021.0121015
PDF

Symbolic Representation-based Melody Extraction using Multiclass Classification for Traditional Javanese Compositions

Author 1: Arry Maulana Syarif
Author 2: Azhari Azhari
Author 3: Suprapto Suprapto
Author 4: Khafiizh Hastuti

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 10, 2021.

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Abstract: Traditional Javanese compositions contain melodies and skeletal melodies. Skeletal melodies are an extraction form of melodies. The melody extraction problem is similar to the chord detection in Western music, where chords are extracted from a melody. This research aims to develop a melody extraction system for traditional Javanese compositions. Melodies which have a time series data structure were designed as a part of the supervised learning problem to be solved using the pattern recognition technique and the Feed-Forward Neural Networks method. The melody data source uses a symbolic format in the form of sheet music. The beats in melodies data are used as the input and notes in skeletal melodies are used as the target. An FFNN multi-class classifier was built with six classes as the targets, where the class represents notes of the musical scale system. The network evaluation was conducted using accuracy, precision, recall, specificity and F-1 score measurements.

Keywords: Melody extraction; symbolic representation-based; multiclass classification; feed-forward neural network; Gamelan

Arry Maulana Syarif, Azhari Azhari, Suprapto Suprapto and Khafiizh Hastuti, “Symbolic Representation-based Melody Extraction using Multiclass Classification for Traditional Javanese Compositions” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121015

@article{Syarif2021,
title = {Symbolic Representation-based Melody Extraction using Multiclass Classification for Traditional Javanese Compositions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121015},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121015},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {Arry Maulana Syarif and Azhari Azhari and Suprapto Suprapto and Khafiizh Hastuti}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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