Gamelan Demung Music Transcription Based on STFT Using Deep Learning

Andi Rokhman Hermawan, E. M. Yuniarno, D. Wulandari
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Abstract

Learning to play a gamelan instrument would be easier when there’s a musical notation guide. The process of converting a musical signal into a notation guide is called transcription. In this paper, we would like to transcript the gamelan music especially the Demung instrument using the Deep Learning method. Each Demung’s note from 6-low until 1-high would be converted to the time-frequency domain using STFT (Short-Time Fourier Transform). Then, those data will be treated as an input for the multilayers perceptron. The training method is a single label of each notation. The output returned by the model is a music roll transcription.
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基于STFT深度学习的佳美兰Demung音乐转录
如果有乐谱指导,学习演奏佳美兰乐器会更容易。把音乐信号转换成乐谱的过程叫做抄写。在本文中,我们想要用深度学习的方法来记录佳美兰音乐,特别是Demung乐器。使用短时傅里叶变换(STFT)将从6-low到1-high的每个Demung音符转换为时频域。然后,这些数据将被视为多层感知器的输入。训练方法是每个符号的单个标签。模型返回的输出是一个音乐卷转录。
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发文量
10
审稿时长
24 weeks
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