6 papers with code • 3 benchmarks • 0 datasets
Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.
This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio-denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.
Unsupervised deep learning methods for solving audio restoration problems extensively rely on carefully tailored neural architectures that carry strong inductive biases for defining priors in the time or spectral domain.
In traditional speech denoising tasks, clean audio signals are often used as the training target, but absolute clean signals are collected from expensive recording equipment or studios with strict environment.