Audio Denoising

6 papers with code • 3 benchmarks • 0 datasets

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Most implemented papers

Learning to Separate Object Sounds by Watching Unlabeled Video

rhgao/Deep-MIML-Network ECCV 2018

Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.

Co-Separating Sounds of Visual Objects

rhgao/co-separation ICCV 2019

Learning how objects sound from video is challenging, since they often heavily overlap in a single audio channel.

Speech Denoising by Accumulating Per-Frequency Modeling Fluctuations

mosheman5/DNP 16 Apr 2019

The method is completely unsupervised and only trains on the specific audio clip that is being denoised.

Speech Denoising Without Clean Training Data: A Noise2Noise Approach

madhavmk/Noise2Noise-audio_denoising_without_clean_training_data 8 Apr 2021

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.

On the Design of Deep Priors for Unsupervised Audio Restoration

vivsivaraman/designaudiopriors 14 Apr 2021

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.

Self-Supervised Speech Denoising Using Only Noisy Audio Signals

liqingchunnnn/only-noisy-training 30 Oct 2021

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.