UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching

• URL: http://arxiv.org/abs/2510.00771v1
• Date: Wed, 01 Oct 2025 11:04:53 GMT
• Title: UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching
• Authors: Woongjib Choi, Sangmin Lee, Hyungseob Lim, Hong-Goo Kang,
• Abstract summary: We present a framework for audio super-resolution that employs a flow matching generative model to capture the conditional distribution of complex-valued spectral coefficients.<n> Experiments show that our model consistently produces high-fidelity 48 kHz audio across diverse upsampling factors.
• Score: 20.92242470770289
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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