https://github.com/lxy5513/Multi-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
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/marialaraa/adresso_analysis
FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
https://github.com/mark-gluzman/NmodelPPO
https://github.com/marload/DeepRL-TensorFlow2
https://github.com/maum-ai/voicefilter
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/mbzuai-nlp/artst
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/mbzuai-nlp/sttatts
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/michael-snower/ppo
https://github.com/microsoft/sig-challenge
Multi-dimensional Speech Quality Assessment in Crowdsourcing
https://github.com/microsoft/strategically_efficient_rl
https://github.com/mightypirate1/PPO_homebrew
https://github.com/milmor/perceptual-losses-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/mindspore-courses/Deep-Reinforcement-Learning-Algorithms-with-MindSpore
https://github.com/miroblog/tf_deep_rl_trader
https://github.com/mit-realm/neuriss
https://github.com/mitmedialab/unet4birds
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/modelscope/FunCodec
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
https://github.com/mohammadiha/bnmf
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization
https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics
https://github.com/morikatron/GAIL_PPO
https://github.com/morikatron/PPO
https://github.com/mpariente/asteroid
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/mravanelli/pySpeechRev
Contaminated speech training methods for robust DNN-HMM distant speech recognition
https://github.com/msaddler/auditory-model-denoising
https://github.com/muhammad-ahmed-ghani/svoice_demo
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/near32/regym
https://github.com/nikhilbarhate99/PPO
https://github.com/nikhilbarhate99/PPO-PyTorch
https://github.com/nikunj-taneja/accendo
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ninatu/mood_challenge
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/nitthilan/pommerman
https://github.com/noufali/VideoML
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/nric/ProximalPolicyOptimizationContinuousKeras
https://github.com/nvlabs/gbrl_sb3
https://github.com/oneTaken/pytorch_fast_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/openpsi-projects/srl
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.