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

Arry Maulana Syarif,Khafiizh Hastuti,Azhari Azhari,Suprapto Suprapto

doi:10.14569/ijacsa.2021.0121015

Arry Maulana Syarif, Khafiizh Hastuti + Show 2 more

Open Access

https://doi.org/10.14569/ijacsa.2021.0121015

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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.

Highlights

This research is part of a program to preserve traditional Javanese music using artificial intelligence methods with the expectation of preserving the authenticity of the compositions throughout the ages
The proposed method successfully combines musical theory in notes sequence pattern recognition by extracting melodies using a multi-class classification based on notation duration and beat rules
Based on the evaluation of the measurement of accuracy, precision, recall, specificity and F1 score on the melody extraction per composition, the performance of the networks in correctly classifying the notes of skeletal melodies from the beats of melodies, including distinguishing extracted notes from targets that are not in their class, has increased compared to the results obtained in training

Summary

Introduction

This research is part of a program to preserve traditional Javanese music using artificial intelligence methods with the expectation of preserving the authenticity of the compositions throughout the ages. Skeletal melodies can be analogous to chords in Western music, and the challenge in this research is similar to the problem of determining chords to accompany the melody. Instead of using audio sources and performing feature extraction, a symbolic representation approach is proposed using sheet music as the dataset source. The proposed method in this research is in line with what [8] stated, the music theory approach without audio can be used as a complementary technique in the field of music information retrieval. The proposed method is similar to that stated by [9] in the context of using all the sequences of notes or chords found in the dataset but the metrical structure in musical theory is still used as a reference to avoid metrical structure errors in composition. The FFNN network was trained using melodies as input and skeletal melodies as the output

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