SAGA-SR: Semantically and Acoustically Guided Audio Super-Resolution
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909814788980736 |
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| author | Im, Jaekwon Nam, Juhan |
| author_facet | Im, Jaekwon Nam, Juhan |
| contents | Versatile audio super-resolution (SR) aims to predict high-frequency components from low-resolution audio across diverse domains such as speech, music, and sound effects. Existing diffusion-based SR methods often fail to produce semantically aligned outputs and struggle with consistent high-frequency reconstruction. In this paper, we propose SAGA-SR, a versatile audio SR model that combines semantic and acoustic guidance. Based on a DiT backbone trained with a flow matching objective, SAGA-SR is conditioned on text and spectral roll-off embeddings. Due to the effective guidance provided by its conditioning, SAGA-SR robustly upsamples audio from arbitrary input sampling rates between 4 kHz and 32 kHz to 44.1 kHz. Both objective and subjective evaluations show that SAGA-SR achieves state-of-the-art performance across all test cases. Sound examples and code for the proposed model are available online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24924 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SAGA-SR: Semantically and Acoustically Guided Audio Super-Resolution Im, Jaekwon Nam, Juhan Audio and Speech Processing Versatile audio super-resolution (SR) aims to predict high-frequency components from low-resolution audio across diverse domains such as speech, music, and sound effects. Existing diffusion-based SR methods often fail to produce semantically aligned outputs and struggle with consistent high-frequency reconstruction. In this paper, we propose SAGA-SR, a versatile audio SR model that combines semantic and acoustic guidance. Based on a DiT backbone trained with a flow matching objective, SAGA-SR is conditioned on text and spectral roll-off embeddings. Due to the effective guidance provided by its conditioning, SAGA-SR robustly upsamples audio from arbitrary input sampling rates between 4 kHz and 32 kHz to 44.1 kHz. Both objective and subjective evaluations show that SAGA-SR achieves state-of-the-art performance across all test cases. Sound examples and code for the proposed model are available online. |
| title | SAGA-SR: Semantically and Acoustically Guided Audio Super-Resolution |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.24924 |