Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions
Deep learning has made a lot of progress in the field of music generation. It now has powerful tools for both preserving traditional music and creating new, innovative compositions. This review explores various and recent deep learning models, such as Long Short-Term Memory (LSTM) networks, Transfor...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04027.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206572852183040 |
|---|---|
| author | Kong Xiangchen |
| author_facet | Kong Xiangchen |
| author_sort | Kong Xiangchen |
| collection | DOAJ |
| description | Deep learning has made a lot of progress in the field of music generation. It now has powerful tools for both preserving traditional music and creating new, innovative compositions. This review explores various and recent deep learning models, such as Long Short-Term Memory (LSTM) networks, Transformer-based models, Reinforcement Learning (RL), and Diffusion-based architectures, and how they are applied to music generation. LSTMs effectively capture temporal dependencies, which are vital for producing coherent melodies and chord progressions. Transformer models, like MUSICGEN and STEMGEN, handle large amounts of data and dependencies efficiently, but they need a lot of computational resources. Reinforcement Learning models, such as MusicRL, combine human feedback to fine-tune AI-generated compositions based on the individual's preferences. Diffusion-based models, like MusicLDM, enhance audio fidelity, though real-time application remains a challenge. The objective of emotion-conditioned models, such as ECMusicLM, is to combine music with emotional cues so that the output has a stronger emotional resonance. However, each model faces its own set of limitations, such as computational inefficiency, data dependency, and challenges in capturing complex emotional nuances. Future research should focus on improving the computational efficiency of these models, expanding training datasets, and integrating more interactive, real-time systems. |
| format | Article |
| id | doaj-art-81027ba5aafc4e44aff2c79c9e311ed9 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-81027ba5aafc4e44aff2c79c9e311ed92025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700402710.1051/itmconf/20257004027itmconf_dai2024_04027Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future DirectionsKong Xiangchen0Computer Science, University of California, DavisDeep learning has made a lot of progress in the field of music generation. It now has powerful tools for both preserving traditional music and creating new, innovative compositions. This review explores various and recent deep learning models, such as Long Short-Term Memory (LSTM) networks, Transformer-based models, Reinforcement Learning (RL), and Diffusion-based architectures, and how they are applied to music generation. LSTMs effectively capture temporal dependencies, which are vital for producing coherent melodies and chord progressions. Transformer models, like MUSICGEN and STEMGEN, handle large amounts of data and dependencies efficiently, but they need a lot of computational resources. Reinforcement Learning models, such as MusicRL, combine human feedback to fine-tune AI-generated compositions based on the individual's preferences. Diffusion-based models, like MusicLDM, enhance audio fidelity, though real-time application remains a challenge. The objective of emotion-conditioned models, such as ECMusicLM, is to combine music with emotional cues so that the output has a stronger emotional resonance. However, each model faces its own set of limitations, such as computational inefficiency, data dependency, and challenges in capturing complex emotional nuances. Future research should focus on improving the computational efficiency of these models, expanding training datasets, and integrating more interactive, real-time systems.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04027.pdf |
| spellingShingle | Kong Xiangchen Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions ITM Web of Conferences |
| title | Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions |
| title_full | Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions |
| title_fullStr | Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions |
| title_full_unstemmed | Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions |
| title_short | Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions |
| title_sort | deep learning in music generation a comprehensive investigation of models challenges and future directions |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04027.pdf |
| work_keys_str_mv | AT kongxiangchen deeplearninginmusicgenerationacomprehensiveinvestigationofmodelschallengesandfuturedirections |