Papers with Code 아카이브
Speech Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yxlu-0102/MP-SENet
공식 코드MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra
https://github.com/zelokuo/VPIDM
공식 코드Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement
https://github.com/zhangyuewei98/ofif-net
공식 코드Speech Enhancement with Overlapped-Frame Information Fusion and Causal Self-Attention
https://github.com/zhenghuatan/gmm-ubm_map_sv
공식 코드rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
https://github.com/zhenghuatan/rVAD
공식 코드rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
https://github.com/zhenghuatan/rvadfast
공식 코드rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
https://gitlab.uni-oldenburg.de/hura4843/deep-mfmvdr
공식 코드Deep Multi-Frame MVDR Filtering for Single-Microphone Speech Enhancement
https://bitbucket.org/act-lab/release
https://github.com/170928/-Review-Generative-Adversarial-Imitation-Learning
https://github.com/170928/-Review-Proximal-Policy-Optimization-Algorithms
https://github.com/2mawi2/master-thesis-experiments
https://github.com/ASzot/ppo-pytorch
https://github.com/Adk2001tech/GAN-Image-Super-Resolution
Super-Resolution with Deep Convolutional Sufficient Statistics
https://github.com/Akella17/Voice_Style_Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AlberetOZ/MIL_test_noise
https://github.com/Alina9/Style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AloneGu/keras_tf_neural_img
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Apogentus/style-transfer-with-strength-control
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AppleHolic/source_separation
https://github.com/Aravind-11/AI-Gaming
https://github.com/Aravind-11/IITM_Saastra
https://github.com/Aravind-11/Multi-Agent-RL
https://github.com/AshivDhondea/DFTS_TF2
DFTS2: Simulating Deep Feature Transmission Over Packet Loss Channels
https://github.com/AtharvBhat/Plus-Ultra
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Audio-WestlakeU/FullSubNet/blob/main/audio_zen/acoustics/rvb.py
Speech Dereverberation with a Reverberation Time Shortening Target
https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py
https://github.com/BerkeleyLearnVerify/VerifAI
https://github.com/BrianPulfer/PapersReimplementations
https://github.com/CODEJIN/RHRNet
RHR-Net: A Residual Hourglass Recurrent Neural Network for Speech Enhancement
https://github.com/CSautier/Breakout
https://github.com/CSautier/PongBot
https://github.com/CYetlanezi/Proyecto-Opti
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Chad4545/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChengBinJin/Real-time-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ContigoAI/tf1-phase-aware-speech-enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.