Automatic note generator for Javanese gamelan music accompaniment using deep learning

Arik Kurniawati, E. M. Yuniarno, Y. Suprapto, Aditya Nur Ikhsan Soewidiatmaka
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引用次数: 2

Abstract

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.
使用深度学习的爪哇佳美兰音乐伴奏自动音符发生器
爪哇佳美兰是一种来自印度尼西亚的传统音乐形式,具有多种风格和模式。其中一种模式是Bonang Barung和Bonang Penerus乐器的和谐音乐。当演奏佳美兰时,结果的模式可以根据音乐的节奏或动态而变化,这对于不熟悉佳美兰规则和符号系统的新手来说是具有挑战性的,因为它只提供旋律音符。与现代音乐不同,在现代音乐中,所有乐器的和声音符都是相同的,爪哇佳美兰的和声对于建立歌曲的特征至关重要。随着技术的进步,音乐创作可以在没有人参与的情况下自动生成,这已经成为音乐生成研究的一个趋势。本研究提出了一种使用双向长期记忆(BiLSTM)网络生成和声伴奏音符的方法,并将其与循环神经网络(RNN)和长期记忆(LSTM)模型进行比较,后者使用数字符号表示音乐数据,使爪哇佳美兰和声音乐的变化更容易学习。这种方法代替了佳美兰作曲家完成歌曲中所有乐器的符号。为了评估产生的谐波音乐,音符距离、动态时间扭曲(DTW)和相互关联技术被用来测量系统产生的结果与佳美兰作曲家的创作之间的距离。此外,提取音频特征并用于音频可视化。实验结果表明,所有模型在使用歌曲的所有特征时都能产生更好的准确率结果,达到90%左右,而仅使用2个特征(节奏和旋律音符)的准确率达到65-70%。此外,与LSTM(+92%)和RNN(+90%)产生的和声相比,BiLSTM模型产生的和声与原始音乐更相似(+93%)。本研究可应用于爪哇佳美兰音乐的演奏。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Advances in Intelligent Informatics
International Journal of Advances in Intelligent Informatics Computer Science-Computer Vision and Pattern Recognition
CiteScore
3.00
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