Music generation with deep learning techniques
This report demonstrated the use of a deep convolutional generative adversarial network (DCGAN) in generating expressive music with dynamics. The existing deep learning models for music generation were reviewed. However, most research focused on musical composition and removed expressive attributes...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/148097 |
| _version_ | 1824454819941711872 |
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| author | Toh, Raymond Kwan How |
| author2 | Alexei Sourin |
| author_facet | Alexei Sourin Toh, Raymond Kwan How |
| author_sort | Toh, Raymond Kwan How |
| collection | NTU |
| description | This report demonstrated the use of a deep convolutional generative adversarial network (DCGAN) in generating expressive music with dynamics. The existing deep learning models for music generation were reviewed. However, most research focused on musical composition and removed expressive attributes during data preprocessing, which resulted in mechanical-sounding, generated music. To address the issue, music elements such as pitch, time, velocity were extracted from MIDI files and encoded with piano roll data representation. With the piano roll data representation, DCGAN learned the data distribution from the given dataset and generated new data derived from the same distribution. The generated music was evaluated based on its incorporation of music dynamics and a user study. The evaluation results verified that DCGAN was capable of generating expressive music comprising of music dynamics and syncopated rhythm. |
| first_indexed | 2025-02-19T03:28:23Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148097 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:28:23Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1480972021-04-23T13:24:50Z Music generation with deep learning techniques Toh, Raymond Kwan How Alexei Sourin School of Computer Science and Engineering assourin@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Visual arts and music::Music::Compositions This report demonstrated the use of a deep convolutional generative adversarial network (DCGAN) in generating expressive music with dynamics. The existing deep learning models for music generation were reviewed. However, most research focused on musical composition and removed expressive attributes during data preprocessing, which resulted in mechanical-sounding, generated music. To address the issue, music elements such as pitch, time, velocity were extracted from MIDI files and encoded with piano roll data representation. With the piano roll data representation, DCGAN learned the data distribution from the given dataset and generated new data derived from the same distribution. The generated music was evaluated based on its incorporation of music dynamics and a user study. The evaluation results verified that DCGAN was capable of generating expressive music comprising of music dynamics and syncopated rhythm. Bachelor of Engineering (Computer Engineering) 2021-04-23T13:24:49Z 2021-04-23T13:24:49Z 2021 Final Year Project (FYP) Toh, R. K. H. (2021). Music generation with deep learning techniques. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148097 https://hdl.handle.net/10356/148097 en SCSE20-0007 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Visual arts and music::Music::Compositions Toh, Raymond Kwan How Music generation with deep learning techniques |
| title | Music generation with deep learning techniques |
| title_full | Music generation with deep learning techniques |
| title_fullStr | Music generation with deep learning techniques |
| title_full_unstemmed | Music generation with deep learning techniques |
| title_short | Music generation with deep learning techniques |
| title_sort | music generation with deep learning techniques |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Visual arts and music::Music::Compositions |
| url | https://hdl.handle.net/10356/148097 |
| work_keys_str_mv | AT tohraymondkwanhow musicgenerationwithdeeplearningtechniques |