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Morse Learning Machine Challenge Catching on with Hams


Experimenter Mauri Niininen, AG1LE, of Lexington, Massachusetts, reports that his Morse Learning Machine Challenge has been catching on among members of the Amateur Radio community. The goal of the competition is to build a machine that can learn how to decode audio files containing Morse code — a better “code trap,” if you will. Niininen said his project has been approved by Kaggle, which bills itself as “the world's largest community of data scientists.” Niininen said that it takes humans many months of effort to learn Morse code, and, after years of practice, the most proficient operators can decode Morse code up to 60 or more words per minute


“Humans have extraordinary ability to quickly adapt to varying conditions, speed and rhythm. We want to find out if it is possible to create a machine learning algorithm that exceeds human performance and adaptability in Morse decoding.”

The computer-generated Morse data for the competition includes various levels of added noise. The signal-to-noise ratio, speed, and message content of the files vary randomly to simulate real-life ham radio HF Morse communication.

“I hope to attract people from the Kaggle community, who are interested in solving new, difficult challenges using their predictive data modeling, computer science and machine learning expertise,” Niininen added.

During the competition, participants will build a learning system capable of decoding Morse code, using development data consisting of 200 WAV audio files containing short sequences of randomized Morse. Data labels are provided for a training set, so participants can self evaluate their systems.

“To evaluate their progress and compare themselves with others, they can submit their prediction results online to get immediate feedback,” he explained. “A real-time Kaggle leader board shows participants their current standing based on their validation set predictions.” Niininen has provided a sample Python Morse decoder to make it easier to get started.

Niininen said that within the first 24 hours of the competition, he had 33 downloads. “We have already 53 downloads of the materials for this competition,” he said on September 5, “and it is growing by the hour, as the word about this challenge is spreading.”




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