吉林大学学报(工学版) ›› 2018, Vol. 48 ›› Issue (3): 866-873.doi: 10.13229/j.cnki.jdxbgxb20170509

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Automatic music composition algorithm based on recurrent neural network

LI Xiong-fei1, FENG Ting-ting2, LUO Shi1, ZHANG Xiao-li1   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012,China;
    2.College of Software, Jilin University, Changchun 130012,China
  • Received:2017-05-18 Online:2018-05-20 Published:2018-05-20
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Abstract: In this paper, we propose an automatic music composition algorithm based on Long Short Term Memory-Recurrent Neural Network (LSTM-RNN). In this algorithm, we first divide music set into set which consists sequences of unit by length, and in the preprocessing we get the Mel frequency cepstrum coefficient as the feature of audio music. Second, the proposed training samples are trained and predicted by LSTM-RNN. Finally, the generated music sequences are joined to get a new music. In order to verify the effectiveness of the algorithm, we carry out an anonymous evaluation of the original music and the music generated by the algorithm. The results show that the algorithm can work well on automatic music composition.

Key words: artificial intelligence, recurrent neural network, automatic music composition algorithm, long short term memory model

CLC Number: 

  • TP181
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