JieGH
FPGA, SIMD, Embedded Sys., AI. APROPOS Project, Marie Skłodowska-Curie Actions Researcher https://www.apropos-itn.eu/
Europe
JieGH's Stars
ffd8/xyscope
XYScope is a library for Processing to render graphics on a vector display (oscilloscope, laser) by converting them to audio.
jhang-jhe-wei/AI-handwriting-recognition-based-on-STM32f746g-DISCOVERY
Artelnics/opennn
OpenNN - Open Neural Networks Library
petewarden/extract_loudest_section
Trims .wav audio files to the loudest section of a given length
iranroman/musicinformationretrieval.com
Instructional notebooks on music information retrieval.
wengsht/cuda-mfcc-gmm
adrianphoulady/weighted-tsetlin-machine-cpp
Weighted Tsetlin Machine in C++
codeplea/genann
simple neural network library in ANSI C
larq/larq
An Open-Source Library for Training Binarized Neural Networks
akhilmathurs/libriadapt
Instructions on downloading and using the LibriAdapt dataset
dpatoukas/EmbFANN
FANN-testing ported for msp430//currenty XOR example only
sonic2000gr/FR6989LCDLibrary
An easy to use library for displaying text and annunciators on the FR6989 Launchpad
ohmyzsh/ohmyzsh
🙃 A delightful community-driven (with 2,400+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python, etc), 140+ themes to spice up your morning, and an auto-update tool that makes it easy to keep up with the latest updates from the community.
alexzielenski/ThemeEngine
OS X App to edit compiled .car files
wavedrom/wavedrom.github.io
Digital timing diagram editor
circulosmeos/gdown.pl
Google Drive direct download of big files
WojciechMigda/Tsetlini
Efficient parallelized implementation of Multilabel Classifier and Regressor Tsetlin Machines
mindsdb/mindsdb
The platform for building AI from enterprise data
H-M-H/Weylus
Use your tablet as graphic tablet/touch screen on your computer.
alexlenail/NN-SVG
Publication-ready NN-architecture schematics.
bricewalker/Hey-Jetson
Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.
CarstenIsert/AIND-VUI-Capstone
Udacity Artificial Intelligence Voice User Interfaces Capstone Project implementing a Neural Net for Speech Recognition
udacity/AIND-VUI-Capstone
AIND Term 2 -- VUI Capstone Project
SIP-Lab/CNN-VAD
A Convolutional Neural Network based Voice Activity Detector for Smartphones
gionanide/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].
lutzroeder/netron
Visualizer for neural network, deep learning and machine learning models
epietrowicz/CNN_Sound_Classification
This code trains a convolutional neural network to classify sound bytes.
jameslyons/python_speech_features
This library provides common speech features for ASR including MFCCs and filterbank energies.
jonnor/embeddedml
Notes on Machine Learning on edge for embedded/sensor/IoT uses
ARM-software/ML-KWS-for-MCU
Keyword spotting on Arm Cortex-M Microcontrollers