Gamelan Melody Generation Using LSTM Networks Controlled by Composition Meter Rules and Special Notes

This study proposes a Gamelan melody
generation system based on three characteristics, which are
the melodic patterns recognition, composition meter rules
that control the duration of notes, and the special notes
(pitches) selection which represent ambiguous rules in
determining the Gamelan musical mode system. Long-Short
Term Memory (LSTM) networks were trained using the
sequence prediction technique to generate symbolic based
Gamelan melodies. The dataset collected from sheet music
was converted into ABC notation format, added with codes
representing the composition meter and special notes, and
restructured into a character-based representation format.
The LSTM network training showed good results in the
melodic patterns recognition but the networks take less than
10 attempts for the LSTM network to successfully generate
one melody. The evaluation was conducted using experts’
judgment. Three generated melodies were sent to experts to
be read, hummed and judged. Overall, the evaluation
results showed that the generated melodies can comply with
the characteristics of the Gamelan melodic patterns, the
composition meter and the special notes