mmWave Radar Aware Dual-Conditioned GAN for Speech Reconstruction of Signals With Low SNR

Abstract:Millimeter-wave (mmWave) radar captures are band-limited and noisy, making for difficult reconstruction of intelligible full-bandwidth speech. In this work, we propose a two-stage speech reconstruction pipeline for mmWave using a Radar-Aware Dual-conditioned Generative Adversarial Network (RAD-GAN), which is capable of performing bandwidth extension on signals with low signal-to-noise ratios (-5 dB to -1 dB), captured through glass walls. We propose an mmWave-tailored Multi-Mel Discriminator (MMD) and a Residual Fusion Gate (RFG) to enhance the generator input to process multiple conditioning channels. The proposed two-stage pipeline involves pretraining the model on synthetically clipped clean speech and finetuning on fused mel spectrograms generated by the RFG. We empirically show that the proposed method, trained on a limited dataset, with no pre-trained modules, and no data augmentations, outperformed state-of-the-art approaches for this specific task. Audio examples of RAD-GAN are available online at this https URL.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2602.22431 [cs.SD]

(or arXiv:2602.22431v2 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2602.22431 Focus to learn more arXiv-issued DOI via DataCite

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