Open AccessFast Recognition using Bar-based Data Mapping for Gamelan Music Genre Classification
Author(s) -
Khafiizh Hastuti,
Pulung Nurtantio Andono,
Arry Maulana Syarif,
Azhari Azhari
Publication year - 2021
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.20.3.2283
Subject(s) - melody , computer science , speech recognition , classifier (uml) , mode (computer interface) , cardinality (data modeling) , artificial neural network , artificial intelligence , musical , pattern recognition (psychology) , data mining , art , visual arts , operating system
This research aims to develop a gamelan music genre classifier based on the musical mode system determined based on the dominant notes in a certain order. Only experts can discriminate the musical mode system of compositions. The Feed Forward Neural Networks method was used to classify gamelan compositions into three musical mode systems. The challenge is to recognize the musical mode system of compositions between the initial melody without having to analyze the entire melody using a small amount of data for the dataset. Instead of conducting a melodic extraction from audio signal data, the text-based skeletal melody data, which is a form of extracted melodic features, are used for the dataset. Unique corpuses are controlled based on the cardinality of the one-to-many relationship, and a data mapping technique based on the bars is used to increase the number of corpuses. The results show that the proposed method is suitable to solve the specified problems, where the accuracy in recognizing the class of unseen compositions between the initial melody achieves at 86.7%.