{"ID":2862024,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00771","arxiv_id":"2510.00771","title":"UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching","abstract":"In this paper, we present a vocoder-free framework for audio super-resolution that employs a flow matching generative model to capture the conditional distribution of complex-valued spectral coefficients. Unlike conventional two-stage diffusion-based approaches that predict a mel-spectrogram and then rely on a pre-trained neural vocoder to synthesize waveforms, our method directly reconstructs waveforms via the inverse Short-Time Fourier Transform (iSTFT), thereby eliminating the dependence on a separate vocoder. This design not only simplifies end-to-end optimization but also overcomes a critical bottleneck of two-stage pipelines, where the final audio quality is fundamentally constrained by vocoder performance. Experiments show that our model consistently produces high-fidelity 48 kHz audio across diverse upsampling factors, achieving state-of-the-art performance on both speech and general audio datasets.","short_abstract":"In this paper, we present a vocoder-free framework for audio super-resolution that employs a flow matching generative model to capture the conditional distribution of complex-valued spectral coefficients. Unlike conventional two-stage diffusion-based approaches that predict a mel-spectrogram and then rely on a pre-trai...","url_abs":"https://arxiv.org/abs/2510.00771","url_pdf":"https://arxiv.org/pdf/2510.00771v2","authors":"[\"Woongjib Choi\",\"Sangmin Lee\",\"Hyungseob Lim\",\"Hong-Goo Kang\"]","published":"2025-10-01T11:04:53Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.AI\",\"cs.SD\",\"eess.SP\"]","methods":"[\"Diffusion Model\"]","has_code":false}