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A2SB: Audio-to-Audio Schrodinger Bridges
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A2SB: Audio-to-Audio Schrodinger Bridges
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Real-world audio is often degraded by numerous factors. This work presents an audio restoration model tailored for high-res music at 44.1kHz. Our model, Audio-to-Audio Schr\"odinger Bridges (A2SB), is capable of both bandwidth extension (predicting high-frequency components) and inpainting (re-generating missing segments). Critically, A2SB is end-to-end requiring no vocoder to predict waveform outputs, able to restore hour-long audio inputs, and trained on permissively licensed music data. A2SB is capable of achieving state-of-the-art band-width extension and inpainting quality on several out-of-distribution music test sets.
Forward citations
Cited by 3 Pith papers
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On the Geometry of Music Bandwidth Extension in Latent Spaces of Audio Codecs
A single mean shift in the latent space of several neural codecs achieves competitive music bandwidth extension on some metrics, implying a largely linear structure.
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AnyBand: Unified Multi-Bandwidth Speech Extension via Frequency-Aware In-Context Spectral Infilling
A single flow-matching model performs speech bandwidth extension across continuously varying cutoff frequencies by treating the observed low-band spectrum as an in-context prompt and infilling the masked high band.
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Schr\"odinger Bridge Mamba for One-Step Speech Enhancement
A Mamba-based speech enhancer trained with Schrödinger Bridge objectives produces strong denoising and dereverberation in one inference step with a low real-time factor.
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