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arXiv · 2602.22431

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

Jash Karani·Adithya Chittem·Deepan Roy·Sandeep Joshi
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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 https://rad-gan-demo-site.vercel.app/.

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Jash Karani, Adithya Chittem, Deepan Roy, Sandeep Joshi. 2026-02-25. mmWave Radar Aware Dual-Conditioned GAN for Speech Reconstruction of Signals With Low SNR. https://arxiv.org/abs/2602.22431

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