https://github.com/neillu23/cdiffuse
공식 코드Conditional Diffusion Probabilistic Model for Speech Enhancement
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/nfurnon/disco
공식 코드https://github.com/nick-nikzad/RDL-SE
공식 코드https://github.com/nikolaikyhne/xlstm-senet
공식 코드https://github.com/nvidia/cleanunet
공식 코드https://github.com/nxtproduct/tunet
공식 코드https://github.com/open-mmlab/amphion
공식 코드Metis: A Foundation Speech Generation Model with Masked Generative Pre-training
https://github.com/overcautious/adenet
공식 코드Look\&Listen: Multi-Modal Correlation Learning for Active Speaker Detection and Speech Enhancement
https://github.com/p-leer/howtotrainyourears
공식 코드https://github.com/philgzl/brever
공식 코드Investigating the Design Space of Diffusion Models for Speech Enhancement
https://github.com/pquochuy/idsegan
공식 코드https://github.com/pquochuy/sasegan
공식 코드Self-Attention Generative Adversarial Network for Speech Enhancement
https://github.com/pranaymanocha/PerceptualAudio
공식 코드A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences
https://github.com/prerak23/RoomParamEstim
공식 코드Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordings
https://github.com/pytorch/audio
공식 코드https://github.com/qinwen-hu/dparn
공식 코드https://github.com/rf5/simple-asgan
공식 코드https://github.com/riaa3102/SESNNet
공식 코드Single Channel Speech Enhancement Using U-Net Spiking Neural Networks
https://github.com/rikorose/deepfilternet
공식 코드https://github.com/roychao19477/pcs
공식 코드Perceptual Contrast Stretching on Target Feature for Speech Enhancement
https://github.com/roychao19477/semamba
공식 코드An Investigation of Incorporating Mamba for Speech Enhancement
https://github.com/ruizhecao96/cmgan
공식 코드https://github.com/samsad35/code-ancogen
공식 코드AnCoGen: Analysis, Control and Generation of Speech with a Masked Autoencoder
https://github.com/santi-pdp/segan
공식 코드https://github.com/serkansulun/deep-music-enhancer
공식 코드On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks
https://github.com/sh01k/MeshRIR
공식 코드https://github.com/shifaspv/SE-FFTNet-tensorflow-implemenatation
공식 코드https://github.com/shifaspv/gruCNN-speech-enhancement-tensorflow
공식 코드A fully recurrent feature extraction for single channel speech enhancement
https://github.com/sinica-slam/kaldi-senan
공식 코드Speech-enhanced and Noise-aware Networks for Robust Speech Recognition
https://github.com/sinica-slam/nytt-remixit
공식 코드https://github.com/slp-rl/aero
공식 코드https://github.com/slp-rl/sc-phase
공식 코드A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement
https://github.com/sony/diffiner
공식 코드Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
https://github.com/sony/hi-resldm
공식 코드High-Resolution Speech Restoration with Latent Diffusion Model
https://github.com/sp-uhh/deep-non-linear-filter
공식 코드Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement
https://github.com/sp-uhh/sgmse
공식 코드Speech Enhancement and Dereverberation with Diffusion-based Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.