V Iashin, E Rahtu - arXiv preprint arXiv:2110.08791, 2021 - arxiv.org
Recent advances in visually-induced audio generation are based on sampling short, low- fidelity, and one-class sounds. Moreover, sampling 1 second of audio from the state-of-the …
An ideal music synthesizer should be both interactive and expressive, generating high- fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural …
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 …
Deep learning in general focuses on training a neural network from large labeled datasets. Yet, in many cases there is value in training a network just from the input at hand. This may …
R Wu, C Zheng - arXiv preprint arXiv:2208.02946, 2022 - arxiv.org
Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that …
A vocoder is a conditional audio generation model that converts acoustic features such as mel-spectrograms into waveforms. Taking inspiration from Differentiable Digital Signal …
Audio inpainting aims to reconstruct missing segments in corrupted recordings. Most of existing methods produce plausible reconstructions when the gap lengths are short, but …
Controllable neural audio synthesis of sound effects is a challenging task due to the potential scarcity and spectro-temporal variance of the data. Differentiable digital signal …
In this manuscript, we propose a novel method to perform audio inpainting, ie, the restoration of audio signals presenting multiple missing parts. Audio inpainting can be …