Joint Enhancement and Bandwidth Extension for Radar Through-Barrier Speech Acquisition

Joint speech enhancement and bandwidth extension are challenging for through-barrier radar vibrometry. Deep learning approaches achieve reasonable success but lack temporal context and bandwidth extension capability for significantly band-limited speech. In this work, we propose a joint speech enhancement and bandwidth extension module consisting of a newly formulated features fusion transformer that leverages temporal context to facilitate the consistency of spectral features of the early extended subbands by large dilation deconvolution operations with the enhancement operation. The proposed EBEnet was evaluated under a through-barrier setup and demonstrated higher speech intelligibility than encoder-decoder-based CNNs.