Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology
The aim of the current work is to examine how deep learning and blockchain technology may be used to create piano music automatically. First, blockchain technology’s Ethernet Proof of Authority (POA) consensus method achieves distributed consensus across the network’s nodes as...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10332175/ |
| _version_ | 1797401023567888384 |
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| author | Pingping Li Bin Wang |
| author_facet | Pingping Li Bin Wang |
| author_sort | Pingping Li |
| collection | DOAJ |
| description | The aim of the current work is to examine how deep learning and blockchain technology may be used to create piano music automatically. First, blockchain technology’s Ethernet Proof of Authority (POA) consensus method achieves distributed consensus across the network’s nodes as a whole. This approach is well suited for the piano music alliance chain since it has an effective consensus efficiency and authentication mechanism. The automatic music generating model is built using these four neural networks after studying the properties of the recurrent neural network, Long and Short-Term Memory network, convolutional neural network (CNN), and multi-column CNN. The number of weighted parameter changes and learning increase together with the function’s iterations, according to tests. As a result, this method can greatly improve the accuracy of the model for music creation. Additionally, the loss value of the loss function constantly falls as the number of iterations rises. Moreover, the methodology put out by other academics takes close to 2.3 seconds to process 1,000 pieces of data from piano scores, whereas the blockchain approach employed in this experiment takes only 1.25 seconds. Therefore, processing data from piano scores by computer using the blockchain concept is highly efficient. Hence, the current work holds significant implications for advancing the intelligence level within the realm of piano composition. |
| first_indexed | 2024-03-09T02:03:52Z |
| format | Article |
| id | doaj.art-82ea1de1f44d4b309d5ea03435855f22 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T02:03:52Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-82ea1de1f44d4b309d5ea03435855f222023-12-08T00:05:45ZengIEEEIEEE Access2169-35362023-01-011113449513450310.1109/ACCESS.2023.333748810332175Automatic Computer Composition for Piano Music via Deep Learning and Blockchain TechnologyPingping Li0Bin Wang1https://orcid.org/0009-0005-1457-2338College of Music and Dance, Hunan First Normal University, Changsha, ChinaCollege of Music and Dance, Hunan University of Arts and Science, Changde, ChinaThe aim of the current work is to examine how deep learning and blockchain technology may be used to create piano music automatically. First, blockchain technology’s Ethernet Proof of Authority (POA) consensus method achieves distributed consensus across the network’s nodes as a whole. This approach is well suited for the piano music alliance chain since it has an effective consensus efficiency and authentication mechanism. The automatic music generating model is built using these four neural networks after studying the properties of the recurrent neural network, Long and Short-Term Memory network, convolutional neural network (CNN), and multi-column CNN. The number of weighted parameter changes and learning increase together with the function’s iterations, according to tests. As a result, this method can greatly improve the accuracy of the model for music creation. Additionally, the loss value of the loss function constantly falls as the number of iterations rises. Moreover, the methodology put out by other academics takes close to 2.3 seconds to process 1,000 pieces of data from piano scores, whereas the blockchain approach employed in this experiment takes only 1.25 seconds. Therefore, processing data from piano scores by computer using the blockchain concept is highly efficient. Hence, the current work holds significant implications for advancing the intelligence level within the realm of piano composition.https://ieeexplore.ieee.org/document/10332175/POA consensus mechanismblockchain technologydeep learninglong and short-term memory networks |
| spellingShingle | Pingping Li Bin Wang Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology IEEE Access POA consensus mechanism blockchain technology deep learning long and short-term memory networks |
| title | Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology |
| title_full | Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology |
| title_fullStr | Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology |
| title_full_unstemmed | Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology |
| title_short | Automatic Computer Composition for Piano Music via Deep Learning and Blockchain Technology |
| title_sort | automatic computer composition for piano music via deep learning and blockchain technology |
| topic | POA consensus mechanism blockchain technology deep learning long and short-term memory networks |
| url | https://ieeexplore.ieee.org/document/10332175/ |
| work_keys_str_mv | AT pingpingli automaticcomputercompositionforpianomusicviadeeplearningandblockchaintechnology AT binwang automaticcomputercompositionforpianomusicviadeeplearningandblockchaintechnology |