Audio Denoising

8 papers with code • 3 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

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.

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.

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.

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.

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

The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs.

BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds

youshanzhang/birdsoundsdenoising 18 Oct 2022

Audio denoising has been explored for decades using both traditional and deep learning-based methods.

The Intel Neuromorphic DNS Challenge

intellabs/intelneuromorphicdnschallenge 16 Mar 2023

A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.