Automatic Music Generator Using Recurrent Neural Network
- DOI
- 10.2991/ijcis.d.200519.001How to use a DOI?
- Keywords
- Music generation; Music composition; Long short-term memory; Gated recurrent units; Subjective evaluation
- Abstract
In this paper, we developed an automatic music generator with midi as the input file. This study uses long short-term memory (LSTM) and gated recurrent units (GRUs) network to build the generator and evaluator model. First, a midi file is converted into a midi matrix in midi encoding process. Then, each midi is trained on a single layer and double stacked layer model of each network as a generator model. Next, classification model, based on LSTM and GRU, are trained and chosen as an objective evaluator to analyze the performance of each generator model which classify each midi based on its musical era. Subjective evaluation is conducted by an interview with volunteer respondents with various backgrounds such as classical music interest, performance, composer, and digital composer. The result shows that the double stacked layer GRU model perform better to resemble the composer pattern in music with 70% score of recall. Moreover, subjective evaluation shows that the generated music is listenable and interesting with the highest score of 6.85 out of 10 on double stacked layer GRU.
- Copyright
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Alexander Agung Santoso Gunawan AU - Ananda Phan Iman AU - Derwin Suhartono PY - 2020 DA - 2020/06/25 TI - Automatic Music Generator Using Recurrent Neural Network JO - International Journal of Computational Intelligence Systems SP - 645 EP - 654 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200519.001 DO - 10.2991/ijcis.d.200519.001 ID - Gunawan2020 ER -