scofir's repositories
AEC-Challenge
AEC Challenge
android_kernel_huawei_mt7l09
Huawei Ascend Mate 7 kernel tree
AudioClassification-Pytorch
The Pytorch implementation of sound classification supports EcapaTdnn, PANNS, TDNN, Res2Net, ResNetSE and other models, as well as a variety of preprocessing methods.
coding-interview-university
A complete computer science study plan to become a software engineer.
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.
DFT-MDFT-filter-banks
design of DFT & MDFT filter banks;
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.
DTLN
Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.
FullSubNet
PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."
MP-SENet
MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra
MS-SNSD
The Microsoft Scalable Noisy Speech Dataset (MS-SNSD) is a noisy speech dataset that can scale to arbitrary sizes depending on the number of speakers, noise types, and Speech to Noise Ratio (SNR) levels desired.
NKF-AEC
Acoustic Echo Cancellation with Nerual Kalman Filtering
PiDTLN
Apply machine learning model DTLN for noise suppression and acoustic echo cancellation on Raspberry Pi
rnnoise
Recurrent neural network for audio noise reduction
sndfilter
Algorithms for sound filters, like reverb, dynamic range compression, lowpass, highpass, notch, etc
THLNet_test
THLNet: two-stage heterogeneous lightweight network for monaural speech enhancement