Identifying ham radio signals used to be easy. Beeps were Morse code, voice was AM unless it sounded like Donald Duck in which case it was sideband. But there are dozens of modes in common use now including TV, digital data, digital voice, FM, and more coming on line every day. [Randaller] used CUDA to build a neural network that could interface with an RTL-SDR dongle and can classify the signals it hears. Since it is a neural network, it isn’t so much programmed to do it as it is trained. The proof of concept has training to distinguish FM, SECAM, and tetra. However, you can train it to recognize other modulation schemes if you want to invest the time into it.
It isn’t that big of a task to identify signals using your built-in neural network. However, this is a great example of a practical neural net and it does open the door to other possibilities. For example, automated monitoring of multiple channels would need something like this.
One interesting tidbit is that the neural network doesn’t really know what it is learning, so input samples could be IQ samples, audio, or even waterfall graphics. You just have to use the same input to train that you want to use during operation. In fact, the code apparently started out as an image classification network from a course by Stanford.