Harnessing Speech Recognition for Enhanced Signal Processing of Satellite Communications

In this work, we propose to consolidate radio frequency communication signals and speech audio into a common data modality: multichannel, time-continuous amplitudes with characteristic spectrograms and a finite symbol alphabet. By putting a portion of the radio spectrum on a similar footing to audio, this may allow us to leverage a great deal of the technological progress achieved by automatic speech recognition (ASR) and readily transfer it to radio frequency machine learning (RFML), a rapidly developing field. To support this claim, we take the leading ASR architecture of wav2vec2 and apply it directly to a challenging dataset of real, low-SNR radio signals captured from satellite telecommunications. Representing the first large-scale application of learned detection and classification of raw signals emitted from a diverse array of active low Earth orbit satellites, the speech-inspired network demonstrates strong proficiency on all tasks and robustness to the degraded signal environment.