Deep gradient reinforcement learning for music improvisation in cloud computing framework
Artificial intelligence (AI) in music improvisation offers promising new avenues for developing human creativity. The difficulty of writing dynamic, flexible musical compositions in real time is discussed in this article. We explore using reinforcement learning (RL) techniques to create more interac...
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2265.pdf |
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| author | Fadwa Alrowais Munya A. Arasi Saud S. Alotaibi Mohammed Alonazi Radwa Marzouk Ahmed S. Salama |
| author_facet | Fadwa Alrowais Munya A. Arasi Saud S. Alotaibi Mohammed Alonazi Radwa Marzouk Ahmed S. Salama |
| author_sort | Fadwa Alrowais |
| collection | DOAJ |
| description | Artificial intelligence (AI) in music improvisation offers promising new avenues for developing human creativity. The difficulty of writing dynamic, flexible musical compositions in real time is discussed in this article. We explore using reinforcement learning (RL) techniques to create more interactive and responsive music creation systems. Here, the musical structures train an RL agent to navigate the complex space of musical possibilities to provide improvisations. The melodic framework in the input musical data is initially identified using bi-directional gated recurrent units. The lyrical concepts such as notes, chords, and rhythms from the recognised framework are transformed into a format suitable for RL input. The deep gradient-based reinforcement learning technique used in this research formulates a reward system that directs the agent to compose aesthetically intriguing and harmonically cohesive musical improvisations. The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG), are leveraged to assess the quality of the improvised music. The proposed model obtains +0.15 of PF, −0.43 of SPD, −0.07 of ADP and 0.0041 NDG, which is a better value than other improvisation methods. |
| format | Article |
| id | doaj-art-4eaff3450a83416bad600003a391429c |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-4eaff3450a83416bad600003a391429c2025-01-26T15:05:18ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e226510.7717/peerj-cs.2265Deep gradient reinforcement learning for music improvisation in cloud computing frameworkFadwa Alrowais0Munya A. Arasi1Saud S. Alotaibi2Mohammed Alonazi3Radwa Marzouk4Ahmed S. Salama5Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, Applied College, King Khalid University, RijalAlmaa, Saudi ArabiaDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Mecca, Saudi ArabiaDepartment of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, New Cairo, EgyptArtificial intelligence (AI) in music improvisation offers promising new avenues for developing human creativity. The difficulty of writing dynamic, flexible musical compositions in real time is discussed in this article. We explore using reinforcement learning (RL) techniques to create more interactive and responsive music creation systems. Here, the musical structures train an RL agent to navigate the complex space of musical possibilities to provide improvisations. The melodic framework in the input musical data is initially identified using bi-directional gated recurrent units. The lyrical concepts such as notes, chords, and rhythms from the recognised framework are transformed into a format suitable for RL input. The deep gradient-based reinforcement learning technique used in this research formulates a reward system that directs the agent to compose aesthetically intriguing and harmonically cohesive musical improvisations. The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG), are leveraged to assess the quality of the improvised music. The proposed model obtains +0.15 of PF, −0.43 of SPD, −0.07 of ADP and 0.0041 NDG, which is a better value than other improvisation methods.https://peerj.com/articles/cs-2265.pdfReinforcement learningGated recurrent unitsMusic improvisationCloud frameworksContainerization |
| spellingShingle | Fadwa Alrowais Munya A. Arasi Saud S. Alotaibi Mohammed Alonazi Radwa Marzouk Ahmed S. Salama Deep gradient reinforcement learning for music improvisation in cloud computing framework PeerJ Computer Science Reinforcement learning Gated recurrent units Music improvisation Cloud frameworks Containerization |
| title | Deep gradient reinforcement learning for music improvisation in cloud computing framework |
| title_full | Deep gradient reinforcement learning for music improvisation in cloud computing framework |
| title_fullStr | Deep gradient reinforcement learning for music improvisation in cloud computing framework |
| title_full_unstemmed | Deep gradient reinforcement learning for music improvisation in cloud computing framework |
| title_short | Deep gradient reinforcement learning for music improvisation in cloud computing framework |
| title_sort | deep gradient reinforcement learning for music improvisation in cloud computing framework |
| topic | Reinforcement learning Gated recurrent units Music improvisation Cloud frameworks Containerization |
| url | https://peerj.com/articles/cs-2265.pdf |
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