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：我们提出了一种快速且模块化的深度学习算法，用于在射电频谱图数据中搜索感兴趣的目标相似信号。首先，在能量检测算法返回的过滤数据上训练自编码器。随后将经典Transformer架构中的位置嵌入层改编为基于频率的嵌入。接着利用自编码器的编码器组件，从射电频谱图中约715赫兹（频率分辨率2.79赫兹/频点）的小窗口提取特征。我们采用该算法在信号集（搜索目标的编码特征）中对给定查询（目标信号的编码表示）进行搜索，输出特征最相似的前k个候选信号。实验验证表明，仅基于原始射电频谱图数据，该算法即可检索出与查询信号外观相似的信号。