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  • 标题:Symbolic Representation-based Melody Extraction using Multiclass Classification for Traditional Javanese Compositions
  • 本地全文:下载
  • 作者:Arry Maulana Syarif ; Azhari Azhari ; Suprapto Suprapto
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2021
  • 卷号:12
  • 期号:10
  • DOI:10.14569/IJACSA.2021.0121015
  • 语种:English
  • 出版社:Science and Information Society (SAI)
  • 摘要: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.
  • 关键词:Melody extraction; symbolic representation-based; multiclass classification; feed-forward neural network; Gamelan
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