apooreapo's Stars
deezer/spleeter
Deezer source separation library including pretrained models.
Jounce/Surge
A Swift library that uses the Accelerate framework to provide high-performance functions for matrix math, digital signal processing, and image manipulation.
Wanderson-Magalhaes/Modern_GUI_PyDracula_PySide6_or_PyQt6
VivekPa/AIAlpha
Use unsupervised and supervised learning to predict stocks
VivekPa/IntroNeuralNetworks
Introducing neural networks to predict stock prices
berndporr/py-ecg-detectors
Popular ECG QRS detectors written in python
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].
MikeMpapa/CNNs-Speech-Music-Discrimination
A deep learning framework for Speech-Music discrimination of continuous audio streams
rafaelmmoreira/PanTompkinsQRS
A portable, ANSI-C implementation of Pan-Tompkins real-time QRS detection algorithm
googleapis/google-api-swift-client
A client generator for APIs described by Google's API Discovery Format.
5yutan5/QtVSCodeStyle
VS Code Style for PySide and PyQt.
matengxiao/QTAutoResize
QT AutoResize
adis300/fft-c
Elegant Fast Fourier Transform in C. Making fft.c from fftpack user-friendly.
gionanide/Cryptography
Crypto projects in python, e.g. Attacks to Vigenere, RSA, Telnet Protocol, Hip Replacement , Vernam cipher, Crack Zip Files, Encryptions RC4, Steganography, Feistel cipher, Superincreasing Knapsac, Elliptic Curve Cryptography, Diffie Hellman & EDF.
develancer/filtfilt
Simple command line utility for zero-phase linear filtering on the fly
etsardou/intelligent_robot_systems_2016
Exercise for the Intelligent Robot Systems course, School of Electrical and Compute Engineering, AUTH, 2016
gionanide/Neural_Machine_Translation
Neural Machine Translation using LSTMs and Attention mechanism. Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture and attention mechanism.
ylboerner/FhirEcg
This project enables users to share their Apple Watch's ECG data with a FHIR server.
anuragajwani/SwiftCPP
yjg30737/pyqt-responsive-label
PyQt QLabel which can resize the font responsively accordance with window's size change
gionanide/OpiRec
Opinion recommendation is a task, recently introduced, for consistently generating a text review and a rating score that a certain user would give to a certain product, which has never seen before. Input information driving recommendation is text reviews and ratings for this product contributed by other users and text reviews submitted by the user under consideration for other products. The aforementioned task faces the same problems emerging in text generation using neural networks, namely repetition and specificity. In this paper, it is experi- mentally demonstrated that by employing coverage loss during training, repetition is reduced without adding extra parameters. Furthermore, the amount of repetition in the generated text review is defined as a measure of the captured information. Such measure is used to improve rating score prediction significantly during testing.
gionanide/ChessGame
Machine learning apply in chess for making optimal decisions, using Neural networks and Supervised learning algorithms
larry-bioengineer/pan_tompkins
Learning to re-create pan tompkins algorithm for R peak detection using Python
gionanide/Nerd-Game
Nerd Game is an app game which tests you in very nerd questions of computer science, maths etc. You have three lives and you can modify your game, if you want to play against the clock. All the questions are in a SQL server which is connected to the GUI part.
helleniccoin/HNC-WALLET-DOWNLOAD
Ready compiled hnc wallets and daemons for: Windows - Lnux - Mac
nicktgr15/yaafe-docker
Yaafe in docker container
dimodimi/AAC-Encoder
A simple AAC encoder
helleniccoin/HNC
The new PoS version of HellenicCoin (HNC)