A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm

We developed a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained an autoencoder on filtered data returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a frequency-based embedding. Next we used the encoder component of the autoencoder to extract features from small (~ 715,Hz with a resolution of 2.79Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data.