Yhe optimal length for a dit is 7.69 so the base of a dit pulse should fit in a 10 samples interval. You can get help from the envelope signal zoom (F). Use this slider to adjust the Morse code speed in Words Per Minute. The detected peak frequency along with its magnitude in dB is displayed in the legend below the x axis D: Controls This is the output of the 16k FFT used to find the frequency of the signal peak. This time line display shows the amplitude of the audio signal C: Spectrum peak detection As much as possible the 8000 S/s sample rate should be selected or its nearest value. 2: Select sample rate among available sample rates for device.An audio input must be selected for the program to work. The "Device" menu item opens a dialog to choose the Audio input. Just contains the "Exit" item to quit application Audio The Neural Network weights are taken from models/default.model you must make sure this file is present. Firstly create and activate a virtual environment: You will need Python3 and virtualenv installed in your system. This is the main application folder containing morseangel.py and its dependencies Start Please check the readme.md file in the notebooks folder for more information. It contains all notebooks from early stage to more elaborated models. This folder contains development Jupyter notebooks. I hope the present materials can serve as a base.ĭetails on the Neural Network (NN) are given in paragraph H of the Usage section. However this is Open Source and contributors are welcome to continue the work and bring enhancements. I cannot spend much more time on this since I already dedicated a lot of time to reach this point. Its purpose is to decode Morse code from the sound coming from an audio device.Īt this stage the model (and thus the "program") shows signs of working although has room for improvement. This is a Python3 application Based on PyQt5 for the GUI and PyTorch for the Deep Neural Network.
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