Papers with Code 아카이브
Speech Enhancement
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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/jcwleo/random-network-distillation-pytorch
https://github.com/jfpettit/flare
https://github.com/jhare96/reinforcement-learning
https://github.com/jonashaag/paperswithcode-speech-enhancement-audiosamples/tree/master/Phase-aware%20Single-stage%20Speech%20Denoising%20and%20Dereverberation%20with%20U-Net
Phase-aware Single-stage Speech Denoising and Dereverberation with U-Net
https://github.com/jonashaag/paperswithcode-speech-enhancement-audiosamples/tree/master/Real%20Time%20Speech%20Enhancement%20in%20the%20Waveform%20Domain
https://github.com/jongornet14/HyperController
https://github.com/jsztompka/MultiAgent-PPO
https://github.com/jsztompka/PPO-demo
https://github.com/judiebig/DR-DiffuSE
Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain
https://github.com/juliusfrost/Research-Paper-Implementations
https://github.com/jusperlee/librispace
SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios
https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution
https://github.com/jw1401/PPO-Tensorflow-2.0
https://github.com/jwr1995/pubsep
Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation
https://github.com/jzi040941/PercepNet
A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
https://github.com/kaiidams/soundstream-pytorch
https://github.com/kaituoxu/Conv-TasNet
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/khalida1wwin/Noise2Noise-audio_denoising_without_clean_training_data
Speech Denoising Without Clean Training Data: A Noise2Noise Approach
https://github.com/kmohanku/Low-Light-Image-Video-Enhancement
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/kooBH/VFWS
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://github.com/ksivaman/super-res
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ku2482/gail-airl-ppo.pytorch
https://github.com/kuntojirohan/speechdenoising
https://github.com/kvsnoufal/neural_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/kynk94/TF2-Image-Generation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/kyutai-labs/moshi
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lcswillems/torch-ac
https://github.com/lgerrets/rl18-curiosity
https://github.com/libreai/neural-painters-x
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/liuyuezhang/pyrl
https://github.com/liyiyuian/Deep-Learning
https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained
https://github.com/lucidrains/audiolm-pytorch
https://github.com/lucidrains/vector-quantize-pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.