Audio Denoising
8 papers with code • 3 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Audio Denoising
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Most implemented papers
Co-Separating Sounds of Visual Objects
rhgao/co-separation
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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
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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
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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
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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
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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
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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
Audio denoising has been explored for decades using both traditional and deep learning-based methods.
The Intel Neuromorphic DNS Challenge
intellabs/intelneuromorphicdnschallenge
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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.
