Recognition
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WIDI Recognition System Professional
Convert any recorded music into MIDI! WIDI Recognition System is capable of converting polyphonic stereo Audio files (MP3, Wave, CD) to MIDI in several seconds. A wide range of presets for different kinds of music are available. WIDI Recognition System is
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Neural Pattern Recognition v.6.0
Neural Pattern Recognition is implemented using Kohonen neural network and can recognize common stock patterns such as Head & Shoulders, Double Tops, Double Bottoms, Rounding Top, Rounding Bottom, Up Trend, Down Trend, Range, Triangle.
Drive Letter Recognition Software v.1 23
Once the Drive Letter Recognition Software is installed, the removable Disk icons and their description change.
Image Recognition Web Test Plugin v.4 30
The Image Recognition Plugin is a powerful add-on that uses machine vision technology for web testing sites that contain non-HTML elements. The Plugin supports Flash applets, Java applets, Movie Player Applets, ActiveX, and all other controls. The
WIDI Recognition System v.4.03
WIDI Recognition System is a polyphonic Audio to MIDI transcriber. The software recognizes CD tracks, MP3 and Wave files and automatically produces polyphonic MIDI files in several seconds. In addition,
Speak-to-Mail Speech Recognition v.1.00.23
Write and send e-mails using voice recognition with Speak-to-Mail. Just open the software and it reads the contact list from your e-mail software and displays an Email template. You can choose recipients,
BANNER Named Entity Recognition System v.2
BANNER is a named entity recognition system intended primarily for biomedical text.
IGesture - Gesture Recognition Framework v.1.0
iGesture is a Java-based gesture recognition framework providing access to multiple gesture recognition algorithms and different input devices.
Leaves Recognition v.1.0
A neuronal network based image recognition application that aims on the automatic recognition of tree species according to available images of the leaves of each species.
M68331 Voice Recognition System v.1.0
This project will show how to implement the Hidden Markov Model approximations of Voice Recognition into embedded and low power systems.