Automatic note generator for Javanese gamelan music accompaniment using deep learning

(1) Arik Kurniawati Mail (Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)
(2) * Eko Mulyanto Yuniarno Mail (Department of Electrical Engineering and Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)
(3) Yoyon Kusnendar Suprapto Mail (Department of Electrical Engineering and Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)
(4) Aditya Nur Ikhsan Soewidiatmaka Mail (Soewidiatmaka Gamelan, Bandung, Indonesia)
*corresponding author


Javanese gamelan is a traditional form of music from Indonesia with a variety of styles and patterns. One of these patterns is the harmony music of the Bonang Barung and Bonang Penerus instruments. When playing gamelan, the resulting patterns can vary based on the music’s rhythm or dynamics, which can be challenging for novice players unfamiliar with the gamelan rules and notation system, which only provides melodic notes. Unlike in modern music, where harmony notes are often the same for all instruments, harmony music in Javanese gamelan is vital in establishing the character of a song. With technological advancements, musical composition can be generated automatically without human participation, which has become a trend in music generation research. This study proposes a method to generate musical accompaniment notes for harmony music using a bidirectional long-term memory (BiLSTM) network and compares it with recurrent neural network (RNN) and long-term memory (LSTM) models that use numerical notation to represent musical data, making it easier to learn the variations of harmony music in Javanese gamelan. This method replaces the gamelan composer in completing the notation for all the instruments in a song. To evaluate the generated harmonic music, note distance, dynamic time warping (DTW), and cross-correlation techniques were used to measure the distance between the system-generated results and the gamelan composer's creations. In addition, audio features were extracted and used to visualize the audio. The experimental results show that all models produced better accuracy results when using all features of the song, reaching a value of around 90%, compared to using only 2 features (rhythm and note of melody), which reached 65-70%. Furthermore, the BiLSTM model produced musical harmonies that were more similar to the original music (+93%) than those generated by the LSTM (+92%) and RNN (+90%). This study can be applied to performing Javanese gamelan music.


Bidirectional LSTM; Deep Learning; Gamelan music; Javanese melody; Musical harmonic



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