Pinned Repositories
beautiful-jekyll
✨ Build a beautiful and simple website in literally minutes. Demo at https://beautifuljekyll.com
BPCSE
Incorporating Broad Phonetic Information for Speech Enhancement
CDiffuSE
Conditional Diffusion Probabilistic Model for Speech Enhancement
DCCRN
implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement" by pytorch
demucs
Code for the paper Music Source Separation in the Waveform Domain
denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
DiffuSE
diffwave
DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.
E2ESE
espnet
End-to-End Speech Processing Toolkit
neillu23's Repositories
neillu23/CDiffuSE
Conditional Diffusion Probabilistic Model for Speech Enhancement
neillu23/DiffuSE
neillu23/E2ESE
neillu23/BPCSE
Incorporating Broad Phonetic Information for Speech Enhancement
neillu23/beautiful-jekyll
✨ Build a beautiful and simple website in literally minutes. Demo at https://beautifuljekyll.com
neillu23/DCCRN
implementation of "DCCRN-Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement" by pytorch
neillu23/demucs
Code for the paper Music Source Separation in the Waveform Domain
neillu23/denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
neillu23/diffwave
DiffWave is a fast, high-quality neural vocoder and waveform synthesizer.
neillu23/espnet
End-to-End Speech Processing Toolkit
neillu23/espnet_model_zoo
ESPnet Model Zoo
neillu23/hyperion
Python toolkit for speech processing
neillu23/idsegan
neillu23/LAVSE
Python codes for Lite Audio-Visual Speech Enhancement.
neillu23/neillu23.github.io
Personal Website
neillu23/s3prl
Self-Supervised Speech Pre-training and Representation Learning Toolkit
neillu23/shinjiwlab.github.io
neillu23/slurp
Repository for SLURP paper
neillu23/speechbrain
A PyTorch-based Speech Toolkit
neillu23/TASLP2020_CHiME4