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Multichannel Speech Enhancement with Deep Neural Networks - Beamforming with Autoencoders
This project applies an autoencoder deep neural network to the multichannel speech enhancement problem. It takes the problem from dataset generation to the model training.
In order to train the model, you need to create a dataset containing the mixture signals and the clean target signals. The dataset is then converted to the magnitude spectrum. You can find use the code snippets in Dataset Generation folder to create your own dataset. Note that you will need to find your own speech dataset and noise dataset. This set ensures the mixture generation and STFT conversion into a structured form.
Train a convolutional neural network to determine content-based similarity between images. This is done with a siamese neural network as shown The model learns from labeled images of similar and dissimiar pairs. The model's objective is to embed similar pairs nearby and dissimilar pairs far apart. This property of the latent space means kNN searches can find similar images. This idea is based on the paper found
For both training and indexing, labeled data will be needed.
This data needed is multiple images of each unique item. Create a JSON file
such as the one seen below. The key of top level items should be
the item_id. Each value should have an images array, which contains
data on each image for that item. Optionally, you can also provide labels
for each item_id, where two items sharing some label will not be
considered dissimilar.
{
"item_id_1": {
"images": [
{
"filename": "relative/path/to/item_1_1.jpg"
},
{
"filename": "relative/path/to/item_1_2.jpg"
}
],
"labels": ["red", "pink"]
},
"item_id_2": {
"images": [
{
"filename": "relative/path/to/item_2_1.jpg"
},
{
"filename": "relative/path/to/item_2_2.jpg"
}
],
"labels": ["blue"]
}
}
For training a model, you will definitely need a GPU. If you do not have one, then we suggest only using a pretrained model provided by Keras's API.
We provide a Jupyter notebook that will walk you through how to train a siamese network. Note you will need a machine with an Nvidia GPU here. DATA=/path/to/images/and/label/files make notebook
If you trained a model, run the following make bash-cpu python utilities.py --export savedmodel --keras-model checkpoints/file_saved_by_notebook.hdf5 Else you can use Google's pretrained model on classification make bash-cpu python utilities.py --export savedmodel
Images need to be embedded and indexed for fast kNN search. GPU and a trained model DATA=/path/to/images/and/label/files make bash-gpu python utilities.py --export balltree \ GPU and Google's pretrained model DATA=/path/to/images/and/label/files make bash-gpu python utilities.py --export balltree \ CPU and Google's pretrained model DATA=/path/to/images/and/label/files make bash-cpu python utilities.py --export balltree \
A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise, as proposed in the paper:
Rodrigues, F. and Pereira, F. Deep Learning from Crowds. In Proc. of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). This implementation is based on Keras and Tensorflow.
Calico is an open source networking and network security solution for containers, virtual machines, and bare-metal workloads. Calico uses standard Linux networking tools to provide two major services for Cloud Native applications: Calicos flexible architecture supports a wide range of deployment options, using modular components, including: Address Management (IPAM). network policy features, plus for those needing a richer set of policy features, Calico network policies. in either public cloud or on-prem deployments. addresses to physical network infrastructure such as Top of Rack routers (ToRs). and Calico network policies.
OpenFlow is a computer network protocol for software-defined networking.
Distropine is distributed P2P network for commenting of sources on the internet. You can discuss any web page regardless of its provider. It embeds itself into your Google Chrome - as a icon next to the address bar, so you can access it comfortably.
Osmius: The Open Source Monitoring Tool is C++ and Java. Monitor "everything" connected to a network with incredible performance. Create and integrate Business Services, SLAs and ITIL processes such as availability management and capacity planning.
LumoGate is a Windows Wifi Hotspot Management Software
LumoGate is a professional Hotspot Billing Software solution designed to provide secured and regulated Wifi Internet access to your customers. For anyone trying to contact me, directly report a bug or feature request for LumoGate you can contact me on: +233242914426 Whatsapp LumoGate locks down Internet access to anyone that's not authenticated or has not agreed to EULA/accepted use policies. LumoGate interrupts Internet access, lets any unauthorized user login, then Internet access is restored. LumoGate is based on Captive Portal Technology. LumoGate acts as a hotspot firewall and management software that control authentication, bandwidth management, session usage, internet traffic log . LumoGate is the simplest yet complete, intuitive tool to manage your public Wifi HotSpot in Internet Cafes, Schools, Hotels, Restaurants, etc.
Traceproto is a traceroute replacement written in c that allows the user to specify the protocol and port to trace to. It currently supports tcp, udp, and icmp traces with the possibility of others in the future. A network server version is also planned.
An open source project of developing Message admin gui based tools for Apache ActiveMQ. The name of tool is ActiveMQBrowser. It aims to implement functionality such as New Message Create/Send, Delete Messages, Forward Messages, Subscribe TOPICs.
ICMP-based network bandwidth measurement tool
BWPing is a tool to measure bandwidth and response times between two hosts using Internet Control Message Protocol (ICMP) echo request/echo reply mechanism. It does not require any special software on the remote host. The only requirement is the ability to respond on ICMP echo request messages. BWPing supports both IPv4 and IPv6 networks.
EchoVNC is a secure, "firewall-friendly" remote-desktop tool with support for VNC, Remote Desktop, and RAdmin servers and viewers. With it, a Windows PC or OSX Mac can be remotely accessed regardless of firewall, router or web-proxy configuration.
Open1X is an open source implementation of the IEEE 802.1X protocol. This project includes support for the supplicant, while other projects (e.g., FreeRADIUS) provide support for the authentication server.
fprobe and fprobe-ulog are NetFlow probes. fprobe - libpcap-based tool that collect network traffic data and emit it as NetFlow flows towards the specified collector. fprobe-ulog - libipulog-based fork of fprobe.
TinyRadius is a small, fast and reliable Java Radius library capable of sending and receiving Radius packets as specified by RFC 2865/2866. TinyRadius is not a fully-fledged Radius server, but helps you to implement Radius services in your application.
BlueCove is a JSR-82 implementation on Java Standard Edition (J2SE) on BlueZ Linux, Mac OS X, WIDCOMM, BlueSoleil and Microsoft Bluetooth stack on WinXPsp2 and newer. Originally developed by Intel Research and currently maintained by volunteers.
Multiband Atheros Driver for WiFi (MADWIFI): Linux driver for 802.11a/b/g Cardbus/PCI/MiniPCI cards using Atheros chipsets.
Serial <-> TCP/IP, Serial <-> UDP/IP, TCP/IP <-> UDP/IP
ScriptCommunicator (scriptable data terminal) script which routes: - serial port (RS232, USB to serial) <-> TCP/IP (Client/Server) - serial port (RS232, USB to serial) <-> UDP/IP - TCP/IP (Client/Server) <-> UDP/IP
Intelligently block brute-force attacks by aggregating system logs
SSHGuard protects hosts from brute-force attacks against SSH and other services. It aggregates system logs and blocks repeat offenders using several firewall backends, including iptables, ipfw, and pf.
In the field of Network Protocols learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.
For now, there are tremendous work opportunities for various IT fields. Most of the courses in Network Protocols is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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