9 citations · 17 across the 21 of their papers we have counts for
4 papers · 1 filter
Solving Audio Inverse Problems with a Diffusion Model
Eloi Moliner, Jaakko Lehtinen, Vesa Välimäki
This paper presents CQT-Diff, a data-driven generative audio model that can, once trained, be used for solving various different audio inverse problems in a problem-agnostic settin…
Realistic Gramophone Noise Synthesis using a Diffusion Model
Eloi Moliner, Vesa Välimäki
This paper introduces a novel data-driven strategy for synthesizing gramophone noise audio textures. A diffusion probabilistic model is applied to generate highly realistic quasipe…
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Eloi Moliner, Vesa Välimäki
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals. Although this topic has been broadly studied during recent years, the particular problem of exte…
A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings
Eloi Moliner, Vesa Välimäki
Enhancing the sound quality of historical music recordings is a long-standing problem. This paper presents a novel denoising method based on a fully-convolutional deep neural netwo…