AG1LE has set up a Kaggle competition whose goal is to build a machine that learns how to decode audio files containing Morse Code. The Kaggle Morse Challenge was approved a couple of days ago.
Kaggle is actually a very interesting website. According to the website, the Kaggle community includes tens of thousands of PhDs from quantitative fields such as computer science, statistics, econometrics, maths and physics, and industries such as insurance, finance, science, and technology. They come from over 100 countries and 200 universities. In addition to the prize money and data, they use Kaggle to meet, learn, network and collaborate with experts from related fields.
According to AG1LE:
During the competition, the participants build a learning system capable of decoding Morse code. To that end, they get development data consisting of 200 .WAV audio files containing short sequences of randomized Morse code. The data labels are provided for a training set so the participants can self-evaluate their systems. To evaluate their progress and compare themselves with others, they can submit their prediction results on-line to get immediate feedback. A real-time leaderboard shows participants their current standing based on their validation set predictions.
I have also provided sample Python Morse decoder to make it easier too get started. While this software is purely experimental version it has some features of the FLDIGI Morse decoder but implemented using Python instead of C++.
You can of course leverage the experimental multichannel CW decoder I recently implemented on FLDIGI or the standalone version of Bayesian decoder written in C++. There is also some new tools I posted to Github.
The competition ends on December 27, which seems kind of short to me, but this is only phase 1. If this competition is successful, a more difficult competition will be set up. This second competition will distortions introduced by normal radio paths and hand-sent code, which can also be more difficult to answer.