Papers with Code 아카이브
Speech Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 18
- 보존된 평가 기록
- 122
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/TanguyJeanneau/white-mirror
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Tee0125/pytorch-fast-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Thehunk1206/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/UesugiErii/tf2-PPO-atari
https://github.com/WalterJohnson0/tf-keras-implementation-of-Image-Style-transformation-network
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Wataru-Nakata/miipher
https://github.com/WilliamYu1993/DAEME/tree/master/images
Speech Enhancement based on Denoising Autoencoder with Multi-branched Encoders
https://github.com/XiangzhuKong/CA-Dense-UNet
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
https://github.com/YangangCao/TRU-Net
Real-time Denoising and Dereverberation with Tiny Recurrent U-Net
https://github.com/YosukeSugiura/SEGAN
https://github.com/YuanPJ/Voice-in-ASR
https://github.com/Zartris/TD3_continuous_control
https://github.com/ZyoungXu/MoSt-DSA
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/abhiskk/fast-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/achaitu/SpeechDenoisingDNN
https://github.com/adamos581/ppo-keras-football
https://github.com/adamwhitakerwilson/speaker_separation
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/adik993/ppo-pytorch
https://github.com/ahmetcanaydemir/sekte
https://github.com/aijialin/style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ailab-pku/rl-framework
https://github.com/alecfilios/Training-Intelligent-game-Agents-through-Competitive-Reinforcement-Learning
https://github.com/alex-petrenko/sample-factory
https://github.com/alexander-prutko/mil-audio-denoising
https://github.com/alexbaumi/PPO-Algorithm
https://github.com/alexbaumi/PPO-Algorithms
https://github.com/alexjc/neural-enhance
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/alexmlamb/blocks_rl_gru_setup
https://github.com/alibaba-damo-academy/funcodec
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
https://github.com/alibabasglab/frcrn
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/alirezakazemipour/ppo-rnd
https://github.com/amanda-lambda/drl-experiments
https://github.com/amanda-lambda/hack-flappy-bird-drl
https://github.com/amartyamukherjee/ppo-packcooling
https://github.com/amaudruz/RL_openaigym
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.