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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12477 |
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| _version_ | 1866915136743145472 |
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| author | Kim, Daewoong Dong, Hao-Wen Jeong, Dasaem |
| author_facet | Kim, Daewoong Dong, Hao-Wen Jeong, Dasaem |
| contents | Modeling the natural contour of fundamental frequency (F0) plays a critical role in music audio synthesis. However, transcribing and managing multiple F0 contours in polyphonic music is challenging, and explicit F0 contour modeling has not yet been explored for polyphonic instrumental synthesis. In this paper, we present ViolinDiff, a two-stage diffusion-based synthesis framework. For a given violin MIDI file, the first stage estimates the F0 contour as pitch bend information, and the second stage generates mel spectrogram incorporating these expressive details. The quantitative metrics and listening test results show that the proposed model generates more realistic violin sounds than the model without explicit pitch bend modeling. Audio samples are available online: daewoung.github.io/ViolinDiff-Demo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12477 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ViolinDiff: Enhancing Expressive Violin Synthesis with Pitch Bend Conditioning Kim, Daewoong Dong, Hao-Wen Jeong, Dasaem Sound Artificial Intelligence Machine Learning Audio and Speech Processing Signal Processing Modeling the natural contour of fundamental frequency (F0) plays a critical role in music audio synthesis. However, transcribing and managing multiple F0 contours in polyphonic music is challenging, and explicit F0 contour modeling has not yet been explored for polyphonic instrumental synthesis. In this paper, we present ViolinDiff, a two-stage diffusion-based synthesis framework. For a given violin MIDI file, the first stage estimates the F0 contour as pitch bend information, and the second stage generates mel spectrogram incorporating these expressive details. The quantitative metrics and listening test results show that the proposed model generates more realistic violin sounds than the model without explicit pitch bend modeling. Audio samples are available online: daewoung.github.io/ViolinDiff-Demo. |
| title | ViolinDiff: Enhancing Expressive Violin Synthesis with Pitch Bend Conditioning |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2409.12477 |