HeYuliang's repositories
fxsound-app
FxSound application and DSP source code
fftw3
DO NOT CHECK OUT THESE FILES FROM GITHUB UNLESS YOU KNOW WHAT YOU ARE DOING. (See below.)
stable-diffusion
A latent text-to-image diffusion model
latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
OouraFFT
Ooura's General Purpose FFT (Fast Fourier/Cosine/Sine Transform) Package
DTLN
Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.
naturalGradICA
MATLAB script of Independent Component Analysis (ICA) based on natural gradient algorithm
MockingBird
🚀AI拟声: 5秒内克隆您的声音并生成任意语音内容 Clone a voice in 5 seconds to generate arbitrary speech in real-time
Voice-Separation-and-Enhancement
A framework for quick testing and comparing multi-channel speech enhancement and separation methods, such as DSB, MVDR, LCMV, GEVD beamforming and ICA, FastICA, IVA, AuxIVA, OverIVA, ILRMA, FastMNMF.
rt60
Calculation of reverberation time (RT60) from impulse response
ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform
ILRMA
MATLAB script of Independent Low-Rank Matrix Analysis (ILRMA)
opus
Modern audio compression for the internet.
rnnoise_v1
Recurrent neural network for audio noise reduction
multichannelNMF
MATLAB script of Multichannel Nonnegative Matrix Factorization
FullSubNet
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
ESC-50
ESC-50: Dataset for Environmental Sound Classification
A-Convolutional-Recurrent-Neural-Network-for-Real-Time-Speech-Enhancement
A minimum unofficial implementation of the "A Convolutional Recurrent Neural Network for Real-Time Speech Enhancement" (CRN) using PyTorch
DNS-Challenge
This repo contains the scripts, models and required files for the Interspeech 2020 Deep Noise Suppression (DNS) Challenge. We are open sourcing clean speech and noise files as well. Participants of this challenge will use the scripts from this repo to create data to train their noise suppressors. They will compare their method with our baseline noise suppressor and report the results.
Comparison-of-Blind-Source-Separation-techniques
Compare AIRES BSS with TRINICON, ILRMA and AuxIVA (online and offline versions)
SpeechDenoisingWithDeepFeatureLosses
Speech Denoising with Deep Feature Losses
Wave-U-Net-for-Speech-Enhancement
Implement Wave-U-Net by PyTorch, and migrate it to the speech enhancement.