Audio inpainting
11 papers with code • 0 benchmarks • 0 datasets
Filling in holes in audio data
Benchmarks
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Most implemented papers
GACELA -- A generative adversarial context encoder for long audio inpainting
We introduce GACELA, a generative adversarial network (GAN) designed to restore missing musical audio data with a duration ranging between hundreds of milliseconds to a few seconds, i. e., to perform long-gap audio inpainting.
Flexible framework for audio reconstruction
The paper presents a unified, flexible framework for the tasks of audio inpainting, declipping, and dequantization.
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
First, we treat the missing samples as latent variables, and derive two expectation-maximization algorithms for estimating the parameters of the model, depending on whether we formulate the problem in the time- or time-frequency domain.
Audio inpainting of music by means of neural networks
andimarafioti/audioContextEncoder
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We studied the ability of deep neural networks (DNNs) to restore missing audio content based on its context, a process usually referred to as audio inpainting.
Audio inpainting with generative adversarial network
nperraud/gan_audio_inpainting
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We improved the quality of the inpainting part using a new proposed WGAN architecture that uses a short-range and a long-range neighboring borders compared to the classical WGAN model.
Approximal operator with application to audio inpainting
In their recent evaluation of time-frequency representations and structured sparsity approaches to audio inpainting, Lieb and Stark (2018) have used a particular mapping as a proximal operator.
Deep Audio Waveform Prior
Arnontu/DeepAudioWaveformPrior
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A network with relevant deep priors is likely to generate a cleaner version of the signal before converging on the corrupted signal.
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.
Msanii: High Fidelity Music Synthesis on a Shoestring Budget
kinyugo/msanii
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In this paper, we present Msanii, a novel diffusion-based model for synthesizing long-context, high-fidelity music efficiently.
Diffusion-Based Audio Inpainting
eloimoliner/audio-inpainting-diffusion
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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.
