Popular GitHub Repositories for Noise Reduction

Recurrent neural network for audio noise reduction

PyTorch implementation of "FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement."

Providing reproducibility in deep learning frameworks

Source code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples.

This is an implementation for the CVPR2020 paper "Learning Invariant Representation for Unsupervised Image Restoration"

[BMVC-20] Official PyTorch implementation of PPDet.

Real-time speech enhancement mobile app using Nested U-Net

NSNet2 Deep Noise Suppression (DNS) package

This is my graduation project in BIT. Title: Noise Reduction Using GRU.

TorchSpectralGate is a PyTorch-based implementation of Spectral Gating, an algorithm for denoising audio signals.

Real-Time Noise Reducer in Android

Audio De-Noiser using a Convolutional Neural Network Architecture built with Tensorflow.js

Modified version of OpenVINO noise_suppression_demo. This version can handle real-time audio stream from microphone and output to headphone.

This thesis applies an autoencoder deep neural network to the multichannel speech enhancement problem. It takes the problem from dataset generation to the model training

TFSpectralGate is a TensorFlow-based implementation of Spectral Gating, an algorithm for denoising audio signals.

CBDNet inference model in Pytorch

Explore Super Image Resolution through sophisticated degradation modeling. Explore this approach to enhance real image quality and elevate your image enhancement capabilities.

Build and end-to-end facial keypoint recognition system using computer vision techniques and deep learning.

An assignment on preprocessing of text including tokenization, stop word removal, noise reduction, and stemming

Basic and Generic AutoEncoder for Multifunctional purposes