Piano harmony automatic adaptation system based on deep reinforcement learning

被引:0
|
作者
Guo, Hui [1 ,2 ]
机构
[1] Tianjin Univ, Sch Educ, Tianjin 300072, Peoples R China
[2] Tianjin Univ Sport, Tianjin 301617, Peoples R China
关键词
Harmonic arrangement; Note detection; Multiple fundamental frequency estimation; Deep reinforcement learning;
D O I
10.1016/j.entcom.2024.100706
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Piano harmony is a combination of multiple notes, which plays a vital role in enriching the expression of melody. However, research on the piano harmony automatic allocation system is insufficient, and the practical application of reinforcement learning technology is scarce. Based on this, this paper determines the key of the piano harmony automatic allocation system through the in-depth analysis of piano harmony, then arranges the piano harmony with the help of reinforcement learning rather than manual methods and designs a complete piano harmony allocation system it provides powerful help for piano music creation. Sufficient experiments show that the piano harmony automatic orchestration system based on reinforcement learning proposed in this paper has high accuracy, and the orchestration effect has a similarity of 93% with the creation of musicians, which the majority of piano scholars have recognized.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] An investigation on the application of deep reinforcement learning in piano playing technique training
    Ji, Chen
    Wang, Dan
    Wang, Huan
    Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
  • [42] Automatic generation control of ubiquitous power Internet of Things integrated energy system based on deep reinforcement learning
    Xi L.
    Yu L.
    Zhang X.
    Hu W.
    Xi, Lei (xilei2014@163.com), 1600, Chinese Academy of Sciences (50): : 221 - 234
  • [43] Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning
    Wang, Tianjing
    Tang, Yong
    CSEE JOURNAL OF POWER AND ENERGY SYSTEMS, 2023, 9 (03): : 937 - 948
  • [44] Deep reinforcement learning for gearshift controllers in automatic transmissions
    Gaiselmann, Gerd
    Altenburg, Stefan
    Studer, Stefan
    Peters, Steven
    ARRAY, 2022, 15
  • [45] Automatic Bug Triaging via Deep Reinforcement Learning
    Liu, Yong
    Qi, Xuexin
    Zhang, Jiali
    Li, Hui
    Ge, Xin
    Ai, Jun
    APPLIED SCIENCES-BASEL, 2022, 12 (07):
  • [46] Deep reinforcement learning architectures for automatic organ segmentation
    Ogrean, Valentin
    Brad, Remus
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 90
  • [47] Automatic Bridge Bidding Using Deep Reinforcement Learning
    Yeh, Chih-Kuan
    Hsieh, Cheng-Yu
    Lin, Hsuan-Tien
    IEEE TRANSACTIONS ON GAMES, 2018, 10 (04) : 365 - 377
  • [48] A Combinatorial Recommendation System Framework Based on Deep Reinforcement Learning
    Zhou, Fei
    Luo, Biao
    Hu, Tianmeng
    Chen, Zihan
    Wen, Yilin
    2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, : 5733 - 5740
  • [49] Deep Reinforcement Learning based Recommender System with State Representation
    Jiang, Peng
    Ma, Jiafeng
    Zhang, Jianming
    2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, : 5703 - 5707
  • [50] A social image recommendation system based on deep reinforcement learning
    Ahmadkhani, Somaye
    Moghaddam, Mohsen Ebrahimi
    PLOS ONE, 2024, 19 (04):