https://github.com/pfnet-research/meta-tasnet
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
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/phanisankar-nidadavolu/phanisankar-nidadavolu.github.io
Feature Enhancement with Deep Feature Losses for Speaker Verification
https://github.com/pheepa/DCUnet
https://github.com/plnguyen2908/UniTalk-ASD-code
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker Detection
https://github.com/pmm09c/ntire-dehazing
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/proteus1991/GridDehazeNet
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/pwc-1/Paper-10/tree/main/speecht5
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/pwc-1/Paper-9/tree/main/3/WaveCRN-master
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
https://github.com/pwc-1/Paper-9/tree/main/4/WaveCRN-master
WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE%2B%2B
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/pytorch/rl/tree/main/examples/ppo
https://github.com/raahatg21/Creating-Artistic-Images
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ray-project/ray/tree/master/rllib
https://github.com/rdadlaney/Audio-Denoiser-CNN
https://github.com/rdutta1999/Patched-Face-Regeneration-v2
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/reinforcement-learning-kr/pg_travel
https://github.com/rickyHong/segan-pytorch-repl
https://github.com/rishikksh20/HiFiplusplus-pytorch
HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
https://github.com/rishikksh20/hifigan-denoiser
https://github.com/riven314/PerceptualLoss-FastAI
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/riyakothari/NeuralStyleTransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/robieta/demucs
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/royjames/room-impulse-responses
dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
https://github.com/rrmina/fast-neural-style-pytorch
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rrrepsac/tb_vc
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rshnn/battleship
https://github.com/ryanchankh/style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/s-sd/task-amenability
https://github.com/samsh19/ML_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/santi-pdp/segan_pytorch
https://github.com/saschaschramm/Pong
https://github.com/saurabh-kataria/PERL-samples
https://github.com/saurjya/asteroid
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/sayannath/Zero-DCE-TFLite
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/sc0ttms/SE-FullSubNet
FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.