Papers with Code 아카이브
Speech Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Speech Enhancement is a signal processing task that involves improving the quality of speech signals captured under noisy or degraded conditions. The goal of speech enhancement is to make speech signals clearer, more intelligible, and more pleasant to listen to, which can be used for various applications such as voice recognition, teleconferencing, and hearing aids. A representative Github project with online demo : ClearerVoice-Studio.
<span style="color:grey; opacity: 0.6">( Image credit: A Fully Convolutional Neural Network For Speech Enhancement )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tidedra/vl-rlhf
https://github.com/tilkb/thermoai
https://github.com/tky823/ssspy
Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices
https://github.com/tmjeong1103/RL_with_RAY
https://github.com/tobiasemrich/SchafkopfRL
https://github.com/tommy19970714/FullSubNetWithASR
FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
https://github.com/tommyvsfu1/RL-NTU
https://github.com/toni-sm/skrl
https://github.com/tonyyouyou/mamba-in-speech
https://github.com/tuanpnm99/RLPongAgent
https://github.com/tuvovan/Zero_DCE_TF
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/udase-chime2023/baseline
RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing
https://github.com/unfinity-core/MetricGAN
https://github.com/usimarit/TiramisuASR
https://github.com/usimarit/vnasr
https://github.com/uvipen/super-mario-bros-ppo-pytorch
https://github.com/uwjunqi/Pytorch-Tensor-Train-Network
Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network
https://github.com/vcadillog/PPO-Mario-Bros-Tensorflow-2
https://github.com/vermashresth/damage-aware-PPO
https://github.com/vheuthe/microbot_rl
https://github.com/vieduy/Neural-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/ArtLine
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/SkinDeep
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/Toon-Me
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/wangshub/RL-Stock
https://github.com/wangzhengfei0730/NIPS2018-AIforProsthetics
https://github.com/wesbz/SoundStream
https://github.com/wilile26811249/Style_Transfer_PyTorch
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/wooseok-shin/MetricGAN-OKD
https://github.com/wooseok-shin/MetricGAN-plus-pytorch
MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement
https://github.com/xiaolianggeitazgh/Conv-Tasnet
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/xiaoyubie1994/dvae_se
Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders
https://github.com/xiawenwen49/ppo
https://github.com/xinan-chen/AP_BWE
https://github.com/xiuyu0000/new_papers_codes/tree/main/ppo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.