volodymyrandrushchak Goto Github PK

antic-s-code

Website Application to share Technology stuff

atom-keyboard-shortcuts

A list of keyboard shortcuts for the Atom text editor

audio-classification

Code for YouTube series: Deep Learning for Audio Classification

bccd_dataset

BCCD (Blood Cell Count and Detection) Dataset is a small-scale dataset for blood cells detection.

cacti

Cacti ™

cordova-plugin-background-geolocation

Sophisticated background and foreground geolocation plugin for Cordova. Tracks user when app is running in background.

cordova-plugin-ble-central

Bluetooth Low Energy (BLE) Central plugin for Apache Cordova (aka PhoneGap)

deep-style-detector

Deep Learning for Medieval script styles in Latin

face-api.js

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

keenethics-book-task

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

provetetrami

react-native-folder-structure

Ideal folder organization scheme for react-native with use redux applications

shlack

simple-ionic-3-app

A simple Ionic 3 app with get requests to a local JSON file. It showcases how to set up a simple service and provides some nice components for your own application.

synthetic-graph-data

Synthetic data generation for graph ML experiments

text_generators

Python code for building a text generator using LSTMs.

trigger-word-detection

Construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection). Trigger word detection is the technology that allows devices like Amazon Alexa, Google Home, Apple Siri, and Baidu DuerOS to wake up upon hearing a certain word. Structure a speech recognition project Synthesize and process audio recordings to create train/dev datasets Train a trigger word detection model and make predictions

wake-up-word-detection

Wake-up-word(WUW)system is an emerging development in recent times. Voice interaction with systems have made life ease and aids in multi-tasking. Apple, Google, Microsoft, Amazon have developed a custom wake-word engine, which are addressed by words such as ‘Hey Siri’. ‘Ok Google’, ‘Cortana’, ‘Alexa’. Our project focuses initially only detection and response to a customized wake-up command. The wake-up command used is “GOLUMOLU”. A wake-up-word detection system search for specific word and reads the word, where it rejects all other words, phrases and sounds. WUW system needs only less memory space, low computational cost and high precision. Artificial Neural Networks(ANN) have reduced the complexity, computational time, latency, thus the efficiency of system has improved. Deep learning has improved the efficiency of automatic speech recognition(SR), where wake word detection is a subset of SR but unlike keyword spotting and voice recognition. A deep learning RNN model is used for the training of the network. RNN are specifically used in case of temporal sequence data and has the ability to process data of different length but of same dimension. For training a model, labelled dataset is needed. We generated three forms of data: golumolu, negative and background. Such that, the model learns circumspectly and attentively detects when specific word found. To start communication with system, the wake word should be delivered. The main task of WUW detection system is to detect the speech, to identify WUW words among spoken words, to check whether the word spoken in altering context.