Popular GitHub Repositories for Noise Addition

Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super Resolution VSR, SRMD, RealSR, Anime4K, RIFE, IFRNet, CAIN, DAIN, and ACNet.

Recurrent neural network for audio noise reduction

Noise supression using deep filtering

QualityScaler - image/video AI upscaler app

Collection of popular and reproducible image denoising works.

? A comprehensive list of open-source datasets for voice and sound computing (95+ datasets).

A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).

CNN-based audio segmentation toolkit. Allows to detect speech, music, noise and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.

Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.

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

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Providing reproducibility in deep learning frameworks

Code and Pretrained Models for Interspeech 2023 Paper "Whisper-AT: Noise-Robust Automatic Speech Recognizers are Also Strong Audio Event Taggers"

RealScaler - image/video AI upscaler app (Real-ESRGAN)

Official pytorch implementation of the paper: "An Edit Friendly DDPM Noise Space: Inversion and Manipulations". CVPR 2024.

Experimental code: sound file preprocessing to optimize Whisper transcriptions without hallucinated texts

The Noise Contrastive Estimation for softmax output written in Pytorch

Tensor network based quantum software framework for the NISQ era

6 nodes for ComfyUI that allows for more control and flexibility over noise to do e.g. variations or "un-sampling"

A set of simple tools for splitting, merging, OP deletion, size compression, rewriting attributes and constants, OP generation, change opset, change to the specified input order, addition of OP, RGB to BGR conversion, change batch size, batch rename of OP, and JSON convertion for ONNX models.