Papers with Code 아카이브
Speech Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/seanmullery/Super-Resolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/sekiguchi92/SpeechEnhancement
Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices
https://github.com/sekiguchi92/eusipco2019
Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices
https://github.com/seloufian/Faster-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/seorim0/DNN-based-Speech-Enhancement-in-the-frequency-domain
FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
https://github.com/serkansulun/deep-music-enhancement
On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks
https://github.com/seungjaeryanlee/osim-rl-helper
https://github.com/shiningsunnyday/mcts-chess
https://github.com/shivsundram/superresolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/shreyesss/PPO-implementation-keras-tensorflow
https://github.com/shuishida/soaprl
https://github.com/siddharthverma314/proximalpolicyoptimization
https://github.com/silvialuu/DRL-2018
https://github.com/sirakik/mprg_fc
https://github.com/sirakik/ppo_football
https://github.com/sleglaive/MLSP-2018
A variance modeling framework based on variational autoencoders for speech enhancement
https://github.com/somvy/MetricGAN
https://github.com/soumik12345/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/soumimaiti/speechlmscore_tool
SpeechLMScore: Evaluating speech generation using speech language model
https://github.com/sp-uhh/disentangled-vae
Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement
https://github.com/sp-uhh/storm
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation
https://github.com/speech-denoising/id-segan
https://github.com/ssarfjoo/improvedsegan
https://github.com/sufengniu/segan_multi
https://github.com/sungwon23/bsrnn
https://github.com/sweetcocoa/DeepComplexUNetPyTorch
https://github.com/synthlabsai/big-math
https://github.com/taku-y/20181125-pybullet
https://github.com/takuseno/ppo
https://github.com/taotaowang97479/MFNet-SpeechEnhancement
https://github.com/tarit21/Speech-Enhancement-module
https://github.com/tatsu-lab/linguistic_calibration
https://github.com/tcmxx/CNTKUnityTools
https://github.com/tensorlayer/RLzoo
https://github.com/thatbrguy/Dehaze-GAN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.