Low-resource symbolic music generation using transformers and chord identification heuristic
Music generation using computers is a task that while interesting, has received comparatively little attention compared to more mainstream problems. With recent advances in deep learning methods, it affords music generation researchers a new range of tools to apply to the cause. One of these is t...
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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/10356/148307 |
| _version_ | 1826111087095840768 |
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| author | Chio, Ting Kiat |
| author2 | Kong Wai-Kin Adams |
| author_facet | Kong Wai-Kin Adams Chio, Ting Kiat |
| author_sort | Chio, Ting Kiat |
| collection | NTU |
| description | Music generation using computers is a task that while interesting, has received comparatively
little attention compared to more mainstream problems. With recent advances in deep learning
methods, it affords music generation researchers a new range of tools to apply to the cause.
One of these is the Transformer, an Attention-driven model that has had considerable success
in modelling sequential data, beating out once state-of-the-art models such as Long-Short
Term Memory models. Even though there are existing efforts to apply Transformer models to
the music generation task, the application of music domain knowledge is still lacking. Models
are still largely trained on data that conveys only pitch, with information underlying the data
such as chord progressions largely being unattended to. The purpose of this research is to test
if the inclusion of chord progression labels can help improve model performance in the task
of music generation, with the goal of producing more realistic and pleasant-sounding pieces.
A template-mask method was used to quickly label large sets of pianoroll data, before the data
was converted into a MIDI-like format with the inclusion of Chord events to represent chords
in a chord progression. A generator Transformer based off the GPT-2 model was then trained
on this data. By observing the pieces generated by the model, we were able to identify
convincing signs that the model was able to learn the concept of chords and chord progressions,
as well as produce pieces that have more recurring structure due to its understanding of these
latent structures of music. |
| first_indexed | 2024-10-01T02:44:53Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/148307 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:44:53Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1483072021-04-29T08:17:34Z Low-resource symbolic music generation using transformers and chord identification heuristic Chio, Ting Kiat Kong Wai-Kin Adams School of Computer Science and Engineering AdamsKong@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Visual arts and music::Music Music generation using computers is a task that while interesting, has received comparatively little attention compared to more mainstream problems. With recent advances in deep learning methods, it affords music generation researchers a new range of tools to apply to the cause. One of these is the Transformer, an Attention-driven model that has had considerable success in modelling sequential data, beating out once state-of-the-art models such as Long-Short Term Memory models. Even though there are existing efforts to apply Transformer models to the music generation task, the application of music domain knowledge is still lacking. Models are still largely trained on data that conveys only pitch, with information underlying the data such as chord progressions largely being unattended to. The purpose of this research is to test if the inclusion of chord progression labels can help improve model performance in the task of music generation, with the goal of producing more realistic and pleasant-sounding pieces. A template-mask method was used to quickly label large sets of pianoroll data, before the data was converted into a MIDI-like format with the inclusion of Chord events to represent chords in a chord progression. A generator Transformer based off the GPT-2 model was then trained on this data. By observing the pieces generated by the model, we were able to identify convincing signs that the model was able to learn the concept of chords and chord progressions, as well as produce pieces that have more recurring structure due to its understanding of these latent structures of music. Bachelor of Engineering (Computer Science) 2021-04-29T08:17:34Z 2021-04-29T08:17:34Z 2021 Final Year Project (FYP) Chio, T. K. (2021). Low-resource symbolic music generation using transformers and chord identification heuristic. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148307 https://hdl.handle.net/10356/148307 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Visual arts and music::Music Chio, Ting Kiat Low-resource symbolic music generation using transformers and chord identification heuristic |
| title | Low-resource symbolic music generation using transformers and chord identification heuristic |
| title_full | Low-resource symbolic music generation using transformers and chord identification heuristic |
| title_fullStr | Low-resource symbolic music generation using transformers and chord identification heuristic |
| title_full_unstemmed | Low-resource symbolic music generation using transformers and chord identification heuristic |
| title_short | Low-resource symbolic music generation using transformers and chord identification heuristic |
| title_sort | low resource symbolic music generation using transformers and chord identification heuristic |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Visual arts and music::Music |
| url | https://hdl.handle.net/10356/148307 |
| work_keys_str_mv | AT chiotingkiat lowresourcesymbolicmusicgenerationusingtransformersandchordidentificationheuristic |