DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915448517296128 |
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| author | Yu, Chin-Yun Martínez-Ramírez, Marco A. Koo, Junghyun Hayes, Ben Liao, Wei-Hsiang Fazekas, György Mitsufuji, Yuki |
| author_facet | Yu, Chin-Yun Martínez-Ramírez, Marco A. Koo, Junghyun Hayes, Ben Liao, Wei-Hsiang Fazekas, György Mitsufuji, Yuki |
| contents | This study introduces a novel and interpretable model, DiffVox, for matching vocal effects in music production. DiffVox, short for ``Differentiable Vocal Fx", integrates parametric equalisation, dynamic range control, delay, and reverb with efficient differentiable implementations to enable gradient-based optimisation for parameter estimation. Vocal presets are retrieved from two datasets, comprising 70 tracks from MedleyDB and 365 tracks from a private collection. Analysis of parameter correlations reveals strong relationships between effects and parameters, such as the high-pass and low-shelf filters often working together to shape the low end, and the delay time correlating with the intensity of the delayed signals. Principal component analysis reveals connections to McAdams' timbre dimensions, where the most crucial component modulates the perceived spaciousness while the secondary components influence spectral brightness. Statistical testing confirms the non-Gaussian nature of the parameter distribution, highlighting the complexity of the vocal effects space. These initial findings on the parameter distributions set the foundation for future research in vocal effects modelling and automatic mixing. Our source code and datasets are accessible at https://github.com/SonyResearch/diffvox. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions Yu, Chin-Yun Martínez-Ramírez, Marco A. Koo, Junghyun Hayes, Ben Liao, Wei-Hsiang Fazekas, György Mitsufuji, Yuki Sound Audio and Speech Processing This study introduces a novel and interpretable model, DiffVox, for matching vocal effects in music production. DiffVox, short for ``Differentiable Vocal Fx", integrates parametric equalisation, dynamic range control, delay, and reverb with efficient differentiable implementations to enable gradient-based optimisation for parameter estimation. Vocal presets are retrieved from two datasets, comprising 70 tracks from MedleyDB and 365 tracks from a private collection. Analysis of parameter correlations reveals strong relationships between effects and parameters, such as the high-pass and low-shelf filters often working together to shape the low end, and the delay time correlating with the intensity of the delayed signals. Principal component analysis reveals connections to McAdams' timbre dimensions, where the most crucial component modulates the perceived spaciousness while the secondary components influence spectral brightness. Statistical testing confirms the non-Gaussian nature of the parameter distribution, highlighting the complexity of the vocal effects space. These initial findings on the parameter distributions set the foundation for future research in vocal effects modelling and automatic mixing. Our source code and datasets are accessible at https://github.com/SonyResearch/diffvox. |
| title | DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.14735 |