{"ID":2862193,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.01082","arxiv_id":"2510.01082","title":"HVAC-EAR: Eavesdropping Human Speech Using HVAC Systems","abstract":"Pressure sensors are widely integrated into modern Heating, Ventilation and Air Conditioning (HVAC) systems. As they are sensitive to acoustic pressure, they can be a source of eavesdropping. We introduce HVAC-EAR, which reconstructs intelligible speech from low-resolution, noisy pressure data with two key contributions: (i) We achieve intelligible reconstruction from as low as 0.5 kHz sampling rate, surpassing prior work limited to hot word detection, by employing a complex-valued conformer with a Complex Unifed Attention Block to capture phoneme dependencies; (ii) We mitigate transient HVAC noise by reconstructing both magnitude and phase of missing frequencies. For the first time, evaluations on real-world HVAC deployments show significant intelligibility up to 1.2 m distance, raising novel privacy concerns.","short_abstract":"Pressure sensors are widely integrated into modern Heating, Ventilation and Air Conditioning (HVAC) systems. As they are sensitive to acoustic pressure, they can be a source of eavesdropping. We introduce HVAC-EAR, which reconstructs intelligible speech from low-resolution, noisy pressure data with two key contribution...","url_abs":"https://arxiv.org/abs/2510.01082","url_pdf":"https://arxiv.org/pdf/2510.01082v2","authors":"[\"Tarikul Islam Tamiti\",\"Biraj Joshi\",\"Rida Hasan\",\"Anomadarshi Barua\"]","published":"2025-10-01T16:29:34Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CR\"]","methods":"[]","has_code":false}