madhavmk / Noise2Noise-audio_denoising_without_clean_training_data

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. 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.

Date Created 2021-03-28 (3 years ago)

Commits 19 (last one 3 years ago)

Stargazers 180 (0 this week)

Watchers 7 (0 this week)

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RepositoryStats indexes 595,856 repositories, of these madhavmk/Noise2Noise-audio_denoising_without_clean_training_data is ranked #195,219 (67th percentile) for total stargazers, and #271,583 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #4,568/17,543.

madhavmk/Noise2Noise-audio_denoising_without_clean_training_data is also tagged with popular topics, for these it's ranked: deep-learning (#3,762/8512), speech (#156/308)

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madhavmk/Noise2Noise-audio_denoising_without_clean_training_data has 1 open pull request on Github, 0 pull requests have been merged over the lifetime of the repository.

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speech autoencoder noise2noise deep-learning noise-removal audio-denoising data-collection noise-reduction speech-denoising audio-enhancement speech-enhancement

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updated: 2024-12-17 @ 06:06pm, id: 352346992 / R_kgDOFQBjcA