{"id":57440,"date":"2026-04-07T09:47:48","date_gmt":"2026-04-07T07:47:48","guid":{"rendered":"https:\/\/www.nae.fr\/2026\/04\/07\/acoustic-imaging-for-uav-detection-dense-beamformed-energy-maps-and-u-net-seld\/"},"modified":"2026-04-07T09:47:48","modified_gmt":"2026-04-07T07:47:48","slug":"acoustic-imaging-for-uav-detection-dense-beamformed-energy-maps-and-u-net-seld","status":"publish","type":"post","link":"https:\/\/www.nae.fr\/en\/2026\/04\/07\/acoustic-imaging-for-uav-detection-dense-beamformed-energy-maps-and-u-net-seld\/","title":{"rendered":"Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD"},"content":{"rendered":"
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\n\nWe introduce a U-net model for 360\u00b0 acoustic source localization formulated as a spherical semantic segmentation task. Rather than regressing discrete direction-of-arrival (DoA) angles, our model segments beamformed audio maps (azimuth & elevation) into regions of active sound presence. Using delay-and-sum (DAS) beamforming on a custom 24-microphone array, we generate signals aligned with drone GPS telemetry to create binary supervision masks. A modified U-Net, trained on frequency-domain representations of these maps, learns to identify spatially distributed source regions while addressing class imbalance via the Tversky loss. Because the network operates on beamformed energy maps, the approach is inherently array-independent and can adapt to different microphone configurations and can be transferred to different microphone configurations with minimal adaptation.\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/blockquote>\n
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\n\nPour en savoir plus : Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD<\/a>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

We introduce a U-net model for 360\u00b0 acoustic source localization formulated as a spherical semantic segmentation task. Rather than regressing discrete direction-of-arrival (DoA) angles, our model segments beamformed audio maps (azimuth & elevation) into regions of active sound presence. Using delay-and-sum (DAS) beamforming on a custom 24-microphone array, we generate signals aligned with drone GPS […]<\/p>\n","protected":false},"author":1,"featured_media":56493,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[34,16],"tags":[35,44,33],"class_list":["post-57440","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation-et-technologique","category-rti","tag-actualites","tag-developpement-des-systemes-intelligents","tag-drones"],"acf":[],"yoast_head":"\nAcoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD - NAE<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.nae.fr\/en\/2026\/04\/07\/acoustic-imaging-for-uav-detection-dense-beamformed-energy-maps-and-u-net-seld\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Acoustic Imaging for UAV Detection: Dense Beamformed Energy Maps and U-Net SELD - NAE\" \/>\n<meta property=\"og:description\" content=\"We introduce a U-net model for 360\u00b0 acoustic source localization formulated as a spherical semantic segmentation task. 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