Papers with Code 아카이브
Speech Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 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/DLR-RM/stable-baselines3
https://github.com/DMIU-ShELL/MOSAIC
https://github.com/DavidCastilloAlvarado/Path-planning-and-Reinforcement-Learning
https://github.com/DevSlem/AINE-DRL
https://github.com/DiegoLeon96/Neural-Speech-Dereverberation
https://github.com/Divsigma/2020-cs213n
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/DmitryUlyanov/texture_nets
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Doyosae/Phase_aware_Deep_Complex_UNet
https://github.com/EconomistGrant/HTFE-tensortrade
https://github.com/Edresson/VoiceSplit
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/ErikFaustmann/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/FMArduini/python-rl
https://github.com/FeliMe/multimodal_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Fuann/TENET
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
https://github.com/GiadaSimionato/Reasoning_Agents_2020
https://github.com/Gouet/Acrobot-PPO
https://github.com/Gouet/Breakout-V0
https://github.com/Gouet/PPO-gym
https://github.com/Gouet/PPO-pytorch
https://github.com/Gregory-Eales/proximal-policy-optimization
https://github.com/GuillaumeVW/NSNet
Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement
https://github.com/HeliosX7/voice-filter
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/IMLHF/SE_DCUNet
https://github.com/IanChan711/ATM_ide
https://github.com/IliasDG/Demucs_Project
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/InSpaceAI/RL-Zoo
https://github.com/Ipsedo/EvoMotion
https://github.com/JL321/Proximal-Policy-Optimization
https://github.com/Jalal-Abdulbaqi/AudioSamples
RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement
https://github.com/JonasRSV/PPO
https://github.com/Josien94/MLiP
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/JusperLee/Conv-TasNet
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/Karan-kapadia/Style-Transfer-karan
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Khrylx/PyTorch-RL
https://github.com/Kumara-Kaushik/proba_v_challenge
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.