Papers with Code 아카이브
Speech Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 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/yakhyo/Fast-Neural-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/yangyucheng000/University/tree/main/model-2/bloom
https://github.com/yangyucheng000/University/tree/main/model-3/speecht5
SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing
https://github.com/yaselley/ssl_layerwise_deepfake
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection
https://github.com/yluo42/TAC
FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing
https://github.com/yoavalon/Quadcopter-env
https://github.com/yoavalon/QuadcopterReinforcementLearning
https://github.com/zhanghang1989/PyTorch-Multi-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/zhaozhipeng1997/demucs_ascend910_pytorch
https://github.com/zhr1201/CNN-for-single-channel-speech-enhancement
https://github.com/zhy1001/Fast_Style_Transfer_using_CNN
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/zjlab-ammi/llm4rl
https://github.com/zsz-hst/RL_single_chase_point
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.