Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond
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| Format: | Preprint |
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2025
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| _version_ | 1866915276315951104 |
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| author | Richter-Powell, Jessie Torralba, Antonio Lorraine, Jonathan |
| author_facet | Richter-Powell, Jessie Torralba, Antonio Lorraine, Jonathan |
| contents | We introduce Audio-SDS, a generalization of Score Distillation Sampling (SDS) to text-conditioned audio diffusion models. While SDS was initially designed for text-to-3D generation using image diffusion, its core idea of distilling a powerful generative prior into a separate parametric representation extends to the audio domain. Leveraging a single pretrained model, Audio-SDS enables a broad range of tasks without requiring specialized datasets. In particular, we demonstrate how Audio-SDS can guide physically informed impact sound simulations, calibrate FM-synthesis parameters, and perform prompt-specified source separation. Our findings illustrate the versatility of distillation-based methods across modalities and establish a robust foundation for future work using generative priors in audio tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_04621 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond Richter-Powell, Jessie Torralba, Antonio Lorraine, Jonathan Sound Artificial Intelligence Machine Learning Multimedia Audio and Speech Processing 68T07 I.2.6; H.5.5; H.5.1 We introduce Audio-SDS, a generalization of Score Distillation Sampling (SDS) to text-conditioned audio diffusion models. While SDS was initially designed for text-to-3D generation using image diffusion, its core idea of distilling a powerful generative prior into a separate parametric representation extends to the audio domain. Leveraging a single pretrained model, Audio-SDS enables a broad range of tasks without requiring specialized datasets. In particular, we demonstrate how Audio-SDS can guide physically informed impact sound simulations, calibrate FM-synthesis parameters, and perform prompt-specified source separation. Our findings illustrate the versatility of distillation-based methods across modalities and establish a robust foundation for future work using generative priors in audio tasks. |
| title | Score Distillation Sampling for Audio: Source Separation, Synthesis, and Beyond |
| topic | Sound Artificial Intelligence Machine Learning Multimedia Audio and Speech Processing 68T07 I.2.6; H.5.5; H.5.1 |
| url | https://arxiv.org/abs/2505.04621 |