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.

翻译：我们开发了一个快速和模块化深层学习算法,以寻找对射频光谱数据感兴趣的表面信号。 首先,我们用一种能源检测算法对一个经过过滤的数据进行了自动编码器的培训。 我们随后将一个定位嵌入层从古典变异器结构改成一个基于频率的嵌入层。 接下来,我们用自动编码器的编码元件从小型(~ 715Hz,分辨率为2. 79Hz / 频率 bin) 窗口中提取特征。 我们用我们的算法搜索一套信号(已编码的搜索信号)的某个特定查询(有兴趣的加密信号),以产生具有类似特征的顶级候选人。 我们成功地证明算法只从原始的无线电光谱数据中提取了类似外观信号。</s>