We really like the idea here, and wonder if it can be expanded upon to visually decode signals based on their waterfall signatures using TensorFlow. Whether it be the weird, dissonant wail of PSK-31 or the angry buzzing of PACTOR, the goings-on across the bands no longer have to remain a mystery. With a five to ten second sample of a signal, captured either live over a microphone or from a recording, he is able to identify the signal automatically. The database contains hashes for an FFT of each sample, which can be easily searched.
used a homebrew version of the app to search a SQL-lite database of audio fingerprints populated not with a playlist of popular music, but with samples from every known signal type in the Signal Identification Wiki. Shazam is a popular smartphone app that listens to a few seconds of a song, creates an audio fingerprint of it, and searches a massive database of songs for a match. has made progress toward automatically classifying unknown signals by modifying a Shazam-like app. Now, the ham bands are full of exotic signals encoding all manner of digital signals, each one with a unique sound and unique demodulation needs. Spinning the dial up and down the bands, one heard familiar sounds – the staccato of Morse, the of sideband voice transmissions, and the occasional flute-like warble of radioteletype signals. Time was once that the amateur radio bands were an aurally predictable place.
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