Pinned Repositories
administrative
Project management related documents for scikit-learn
ADSP_Tutorials
Advanced Signal Processing Notebooks and Tutorials
ADSP_Tutorials-1
Advanced Digital Signal Processing Notebooks and Tutorials
angular-interview-questions
List of 300 Angular Interview Questions and answers
autokeras
AutoML library for deep learning
autoscraper
A Smart, Automatic, Fast and Lightweight Web Scraper for Python
bank-kata-javascript
bank-kata-js
bank-kata-js-1
bank-kata-js-2
Kata para entender y practicar con outside-in tdd y test doubles
codeur66's Repositories
codeur66/PythonWebScraping-AWSDeployment
Extracting data from the IT Jobs Watch website using web scraping with Python and creating a CI/CD pipeline using Docker and Jenkins to deploy the Python application onto an AWS EC2 Instance.
codeur66/cheatsheets
Frequently Used bash/shell, ffmpeg, and imagemagick Commands
codeur66/WebApp-CI-CD
A basic Python web app with a functioning Jenkins CI/CD pipeline
codeur66/python-audio
Some Jupyter notebooks about audio signal processing with Python
codeur66/xarray-data
Data repository for xarray examples
codeur66/wheelhouse-uploader
Script to help maintain a wheelhouse folder on a cloud storage.
codeur66/Python-for-Signal-Processing
Notebooks for "Python for Signal Processing" book
codeur66/Speech_Signal_Processing_and_Classification
Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].
codeur66/MRSP_Notebooks
Multirate Signal Processing Tutorial
codeur66/PRML
PRML algorithms implemented in Python
codeur66/probability
Probabilistic reasoning and statistical analysis in TensorFlow
codeur66/energy_charts
codeur66/LearnPython
以撸代码的形式学习Python
codeur66/eegssl
Experiments on Self-Supervised Learning on EEG data
codeur66/build-stats
A Jupyter Notebook for Jenkins Build Analysis
codeur66/threadpoolctl
Python helpers to limit the number of threads used in threadpool-backed parallelism for C-libraries
codeur66/Learn-Statistical-Learning-Method
Implementation of Statistical Learning Method, Second Edition.《统计学习方法》第二版,算法实现。
codeur66/joblib-feedstock
A conda-smithy repository for joblib.
codeur66/loky-feedstock
A conda-smithy repository for loky.
codeur66/pandoc_resume
The Markdown Resume
codeur66/keras-applications
Reference implementations of popular deep learning models.
codeur66/devopsProj1_PythonServer
Python Server with Docker file and Jenkins file which will be used for CI CD pipeline
codeur66/pydata-google-auth
A package providing helpers for authenticating to Google APIs.
codeur66/NeuroKit.py
A Python Toolbox for Statistics and Neurophysiological Signal Processing (EEG, EDA, ECG, EMG...).
codeur66/tensorflow
An Open Source Machine Learning Framework for Everyone
codeur66/tensorboard
TensorFlow's Visualization Toolkit
codeur66/scikit-learn-tutorial
codeur66/notebooks
Some sample IPython notebooks for scikit-learn
codeur66/scikit-learn-wheels
Automated setup to build scikit-learn wheels for released versions.
codeur66/openblas-libs