Speech Denoising using RNNs in Tensorflow
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Updated
Apr 20, 2018 - Jupyter Notebook
Speech Denoising using RNNs in Tensorflow
Machine Learning, Neural Nets, Deep Learning basics
A statistical model-based Speech Enhancement Using MMSE-STSA
A curated list of awesome Speech Enhancement papers, libraries, datasets, and other resources.
A solution of "Bandai Namco Data Science Challenge." The task is to estimate clean mel-spectrogram by removing noise from artificially contaminated noisy one.
Removing noise from speech using 1-D & 2-D Convolutional Neural Network (CNN)
Deep Recurrent Neural Networks for Source Separation
An implementation of the paper MetricGAN (ICML 2019) in pytorch with some changes.
Time-Frequency Regularized Overlapping Group Shrinkage
About Implementation and training of a deep neural network for speech denoising tasks.
A self-supervised speech denoising strategy named Only-Noisy Training (ONT), which solves the speech denoising problem with only noisy audio signals in audio space for the first time.
A neural network for end-to-end speech denoising
Tensorflow 2.x implementation of the DTLN real time speech denoising model. With TF-lite, ONNX and real-time audio processing support.
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 denoisi…
Denoising speech audio using different types of CNN's combined with MFCC's.
Official PyTorch Implementation of CleanUNet (ICASSP 2022)
NNSE (Neural Network Speech Enhancement) is a speech-denoiser optimized to run on Ambiq's low power platform
Unofficial implementation of ResGrad: Residual Denoising Diffusion Probabilistic Models for Text to Speech
This repository contains a PyTorch implementation of U-Net applied on mel-spectograms of audio files for speech denoising.
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