Expressing and Developing Melodic Phrases in Gamelan Skeletal Melody Generation Using Genetic Algorithm

A novel approach was proposed to develop a composition generation system for creating note sequences representing question-and-answer phrases (QAPs) in melodic phrases. Using a small dataset containing fewer than 10 compositions, the system is expected to generate new melodies that extend beyond the limitations of the original dataset. The modeling of note sequence data and selecting appropriate notes in specific sequences within the melody were chosen for gamelan skeletal melody generation using the Genetic Algorithm (GA) method. Gamelan, a traditional musical ensemble from Java, was selected as the focus of this research, with experiments centered on the gamelan skeletal melody, which plays a role similar to chord progressions in Western music. The evaluation was conducted by statistically analyzing the sequence of notations generated from the dataset and through expert testing. The experimental results demonstrate that the proposed method can generate compositions with well-formed melodic phrases through a small dataset containing varying lengths, data mapping, feature selection based on QAP patterns, and GA. GA can enhance creativity in creating note sequences and new QAP patterns, including novel patterns.