techxzen's Stars
MorvanZhou/Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
kaiiiz/hexo-theme-book
[No Longer Maintained] A simple, elegant, book-like hexo theme with some useful features.
zslomo/2019-Autumn-recruitment-experience
2019届秋招面经集合
houshanren/hangzhou_house_knowledge
2017年买房经历总结出来的买房购房知识分享给大家,希望对大家有所帮助。买房不易,且买且珍惜。Sharing the knowledge of buy an own house that according to the experience at hangzhou in 2017 to all the people. It's not easy to buy a own house, so I hope that it would be useful to everyone.
rbgirshick/py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
albumentations-team/albumentations
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
gcr/torch-residual-networks
This is a Torch implementation of ["Deep Residual Learning for Image Recognition",Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun](http://arxiv.org/abs/1512.03385) the winners of the 2015 ILSVRC and COCO challenges.
kuangliu/pytorch-cifar
95.47% on CIFAR10 with PyTorch
LeNPaul/Lagrange
A minimalist Jekyll theme for running a personal blog powered by Jekyll and GitHub Pages
MLjian/TextClassificationImplement
’达观杯‘文本智能处理挑战赛,文本分类任务的实现,包括一些传统的监督学习算法和深度学习算法,主要基于sklearn/xgb/lgb/pytorch包实现。
merrymercy/tvm-mali
Optimizing Mobile Deep Learning on ARM GPU with TVM
leftthomas/SEGAN
A PyTorch implementation of SEGAN based on INTERSPEECH 2017 paper "SEGAN: Speech Enhancement Generative Adversarial Network"
EdwardLin2014/CNN-with-IBM-for-Singing-Voice-Separation
santi-pdp/segan
Speech Enhancement Generative Adversarial Network in TensorFlow
linksense/ConvolutionaNeuralNetworksToEnhanceCodedSpeech
In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to G.711, even en par with uncoded speech.
yashiro32/speech_recognition
Speech recognition
Perception-and-Neurodynamics-Laboratory/Matlab-toolbox-for-DNN-based-speech-separation
This folder contains Matlab programs for a toolbox for supervised speech separation using deep neural networks (DNNs).
yongxuUSTC/DNN-SpeechEnhancement
DNN-based speech enhancement using Tensorflow by Haoyu Li (Tokyo univ.)
yongxuUSTC/sednn
deep learning based speech enhancement using keras or pytorch, make it easy to use
adiyoss/GCommandsPytorch
ConvNets for Audio Recognition using Google Commands Dataset
ARM-software/ML-KWS-for-MCU
Keyword spotting on Arm Cortex-M Microcontrollers
castorini/honk
PyTorch implementations of neural network models for keyword spotting
shinseung428/gan_numpy