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Found 10 papers, 5 papers with code
A Diffusion-Based Generative Equalizer for Music Restoration
This paper presents a novel approach to audio restoration, focusing on the enhancement of low-quality music recordings, and in particular historical ones.
Diffusion Models for Audio Restoration
Here, we aim to show that diffusion models can combine the best of both worlds and offer the opportunity to design audio restoration algorithms with a good degree of interpretability and a remarkable performance in terms of sound quality.
Noise Morphing for Audio Time Stretching
This letter introduces an innovative method to enhance the quality of audio time stretching by precisely decomposing a sound into sines, transients, and noise and by improving the processing of the latter component.
Blind Audio Bandwidth Extension: A Diffusion-Based Zero-Shot Approach
In cases where the lowpass degradation is unknown, such as in restoring historical audio recordings, this becomes a blind problem.
Neural modeling of magnetic tape recorders
The sound of magnetic recording media, such as open-reel and cassette tape recorders, is still sought after by today's sound practitioners due to the imperfections embedded in the physics of the magnetic recording process.
Diffusion-Based Audio Inpainting
The proposed method uses an unconditionally trained generative model, which can be conditioned in a zero-shot fashion for audio inpainting, and is able to regenerate gaps of any size.
Solving Audio Inverse Problems with a Diffusion Model
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 setting.
Realistic Gramophone Noise Synthesis using a Diffusion Model
A diffusion probabilistic model is applied to generate highly realistic quasiperiodic noises.
BEHM-GAN: Bandwidth Extension of Historical Music using Generative Adversarial Networks
Audio bandwidth extension aims to expand the spectrum of narrow-band audio signals.
A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings
Enhancing the sound quality of historical music recordings is a long-standing problem.
