Pinned Repositories
1D-Speech-Emotion-Recognition
Speech Emotion Recognition from raw speech signals using 1D CNN-LSTM
awesome-matlab
A curated list of awesome Matlab frameworks, libraries and software.
CNN_LeNet-5_onedimension
about how to use CNN with one dimensional signal
Data-Augmentation-For-Wearable-Sensor-Data
A sample code of data augmentation methods for wearable sensor data (time-series data)
EEGNet
[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
Human-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones
This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.
Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables
The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier
midi-lstm-gan
Using LSTMs and GANs to Generate Music from MIDI Files (APM Fall 2018)
SleepEEGNet
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
xnmssn's Repositories
xnmssn/SleepEEGNet
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
xnmssn/1D-Speech-Emotion-Recognition
Speech Emotion Recognition from raw speech signals using 1D CNN-LSTM
xnmssn/awesome-matlab
A curated list of awesome Matlab frameworks, libraries and software.
xnmssn/CNN_LeNet-5_onedimension
about how to use CNN with one dimensional signal
xnmssn/Data-Augmentation-For-Wearable-Sensor-Data
A sample code of data augmentation methods for wearable sensor data (time-series data)
xnmssn/EEGNet
[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
xnmssn/Human-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones
This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.
xnmssn/Human-Activity-Recognition-with-Neural-Network-using-Gyroscopic-and-Accelerometer-variables
The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.
xnmssn/LSTM-Human-Activity-Recognition
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier
xnmssn/midi-lstm-gan
Using LSTMs and GANs to Generate Music from MIDI Files (APM Fall 2018)
xnmssn/modulation-recognition-for-wireless-signals
Algorithm for classification of Digital Modulation such as FSK, PSK, ASK, QAM ........
xnmssn/Multilabel-timeseries-classification-with-LSTM
Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.
xnmssn/mygit
xnmssn/OUCML
xnmssn/Spindle
数据表型特征的挖掘
xnmssn/xnmssn
Config files for my GitHub profile.