https://github.com/funcwj/conv-tas-net
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
Papers with Code 아카이브
Speech Enhancement
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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/funcwj/conv-tasnet
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/gaetanserre/l2rpn-2022_ppo-baseline
https://github.com/georgkruse/cleanqrl
https://github.com/github-zbx/ava_datasets
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
https://github.com/gitwukeyi/FSPEN
https://github.com/gmoss1/Kaggle-Halite-IV-RL
https://github.com/goncharom/PPOv1
https://github.com/google/lyra
https://github.com/gordicaleksa/pytorch-neural-style-transfer-fast
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/gordicaleksa/pytorch-nst-feedforward
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/gstoica27/cpg_ppo
https://github.com/guofeng201507/ISA-IPA-2020-05-05-IS1PT-GRP-High5-StockTrading
https://github.com/gwthomas/gtml
https://github.com/haamoon/mmtm
https://github.com/habout632/gans
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/hamishs/JAX-RL
https://github.com/haozh7109/SEGAN-TensorFlow2
https://github.com/harruff/Senior_Project_Repository
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/hatsunexym/YAMAHA-Corp
https://github.com/hdparks/AsteroidsDeepReinforcement
https://github.com/hill-a/stable-baselines
https://github.com/hit-thusz-rookiecj/fullsubnet-plus
FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement
https://github.com/hmhuy0/SIM-RL
https://github.com/huggingface/transformers
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/huylenguyen806/vnasr
https://github.com/ifestus/rl
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail
https://github.com/ikostrikov/pytorch-rl
https://github.com/ilshatus/nn_style_transfer_cv_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/imransalam/JJ-style-transfer-TensorFlow2.0
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/inoryy/reaver
https://github.com/intellisys-lab/stellaris-sc24
https://github.com/jain-abhinav02/VoiceFilter
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/jayChung0302/DeepFilter
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.