https://github.com/kyutai-labs/moshi
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Papers with Code 아카이브
Speech Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Obtain the clean speech of the target speaker by suppressing the background noise. Recent representative github platform ClearerVoice-Studio
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/unispeech
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/mohammadiha/bnmf
Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization
https://github.com/msaddler/auditory-model-denoising
https://github.com/nyrahealth/crisperwhisper
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/olawod/freevc
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/pwc-1/Paper-9/tree/main/1/wavlm
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech 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/sanyuan-chen/unispeech
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
https://github.com/saurabh-kataria/PERL-samples
https://github.com/sp-uhh/sgmse
https://github.com/tencent-ailab/fra-rir
FRA-RIR: Fast Random Approximation of the Image-source Method
https://github.com/vitrioil/Speech-Separation
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
https://github.com/yxlu-0102/MP-SENet
Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.