YXZ-Seven's repositories
AudioSignalProcessingForML
Code and slides of my YouTube series called "Audio Signal Proessing for Machine Learning"
TensorFlow-2.x-Tutorials
TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。
Atlas-GAN
[ICCV 2021] Generative Adversarial Registration for Improved Conditional Deformable Templates
audio
Data manipulation and transformation for audio signal processing, powered by PyTorch
DataScience_MachineLearning
Analysing Big Data(vibration, force and temperature) and designing an AI model(ANN based) to predict the surface roughness of a manufactured product
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
emg-data-analysis
Surface EMG signal - Feature Extraction
Erdre
Erroneous data repair for Industry 4.0.
feature-engineering-book
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
Gearbox-Hybrid-CNN-SVM
The features of nonlinearity and non-stationarity in real systems are often difficult to be extracted. This paper focuses on developing a Convolutional Neural Network (CNN) to obtain features directly from the original vibration signals of a gearbox with different pinion conditions. Experimental data is used to show the efficiency of the presented method. Support Vector Machine (SVM) is utilized to classify feature sets extracted with 1D-CNN. The obtained results show that the features extracted in this method have excellent quality for fault classification without any additional feature selection.
Hiding-Audio-in-Images-using-Deep-Generative-Network-with-Adversarial-Training
In this work, we propose an end-to-end trainable model of Generative Adversarial Networks (GAN) which is engineered to hide audio data in images. Due to the non-stationary property of audio signals and lack of powerful tools, audio hiding in images was not explored well. We devised a deep generative model that consists of an auto-encoder as generator along with one discriminator that are trained to embed the message while, an exclusive extractor network with an audio discriminator is trained fundamentally to extract the hidden message from the encoded host signal. The encoded image is subjected to few common attacks and it is established that the message signal can not be hindered making the proposed method robust towards blurring, rotation, noise, and cropping. The one remarkable feature of our method is that it can be trained to recover against various attacks and hence can also be used for watermarking.
improved_CcGAN
Continuous Conditional Generative Adversarial Networks (CcGAN)
JL-CNN
The code of Joint Learning CNN for Vibration Signal Denoising and Bearing Fault Diagnosis under Unknown Noise Condition.
keras-tuner
Hyperparameter tuning for humans
ml-tool-wear
Anomaly detection on the UC Berkeley milling data set using a disentangled-variational-autoencoder (beta-VAE). Replication of results as described in article "Self-Supervised Learning for Tool Wear Monitoring with a Disentangled-Variational-Autoencoder"
Multi_Sensor_Fusion
Multi-Sensor Fusion (GNSS, IMU, Camera) 多源多传感器融合定位 GPS/INS组合导航 PPP/INS紧组合
Python-for-Signal-Processing
Notebooks for "Python for Signal Processing" book
SAGAN-tensorflow2.0
Tensorflow-2.0 implementation of "Self-Attention Generative Adversarial Networks"
Self-Attention-GAN-Original
Pytorch implementation of Self-Attention Generative Adversarial Networks (SAGAN)
Signal_Feature_Extraction
Hilbert变换提取信号特征的Python实现A Python Implementation of Hilbert Transform to Extract Signal Features
Speech_Feature_Extraction
Feature extraction of speech signal is the initial stage of any speech recognition system.
tuningPlayBook-Chinese
谷歌DeepLearning tuningPlayBook的中文翻译版本
Two-stream-CNN-for-rolling-bear-fault-diagnosis
Based on the dual-flow CNN, a new bearing fault diagnosis model is proposed. The model is composed of 2D-CNN and 1D-CNN. Among them, 2D-CNN takes wavelet time-frequency map as input, and 1D-CNN takes original vibration signal as input. After the feature extraction is implemented by the convolutional layer and the pooling layer, the output of the pooling layer of the two is spliced using a fully connected layer, and then the fault classification is achieved through the fully connected layer
vibration_gan
Gan for time series vibration signals generation task
x-ite-feature-extraction
Scripts for the extraction of statistical descriptors from bio signals of the X-ITE Pain Database (preprocessing included).