Track Role Prediction of Single-Instrumental Sequences
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866910415645048832 |
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| author | Han, Changheon Lee, Suhyun Ko, Minsam |
| author_facet | Han, Changheon Lee, Suhyun Ko, Minsam |
| contents | In the composition process, selecting appropriate single-instrumental music sequences and assigning their track-role is an indispensable task. However, manually determining the track-role for a myriad of music samples can be time-consuming and labor-intensive. This study introduces a deep learning model designed to automatically predict the track-role of single-instrumental music sequences. Our evaluations show a prediction accuracy of 87% in the symbolic domain and 84% in the audio domain. The proposed track-role prediction methods hold promise for future applications in AI music generation and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13286 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Track Role Prediction of Single-Instrumental Sequences Han, Changheon Lee, Suhyun Ko, Minsam Sound Information Retrieval Audio and Speech Processing In the composition process, selecting appropriate single-instrumental music sequences and assigning their track-role is an indispensable task. However, manually determining the track-role for a myriad of music samples can be time-consuming and labor-intensive. This study introduces a deep learning model designed to automatically predict the track-role of single-instrumental music sequences. Our evaluations show a prediction accuracy of 87% in the symbolic domain and 84% in the audio domain. The proposed track-role prediction methods hold promise for future applications in AI music generation and analysis. |
| title | Track Role Prediction of Single-Instrumental Sequences |
| topic | Sound Information Retrieval Audio and Speech Processing |
| url | https://arxiv.org/abs/2404.13286 |