Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.Comment: 6 pages, 9 figure

Aerodynamic imaging by mosquitoes inspires a surface detector for autonomous flying vehicles

Some flying animals use active sensing to perceive and avoid obstacles. Nocturnal mosquitoes exhibit a behavioral response to divert away from surfaces when vision is unavailable, indicating a short-range, mechanosensory collision-avoidance mechanism. We suggest that this behavior is mediated by perceiving modulations of their self-induced airflow patterns as they enter a ground or wall effect. We used computational fluid dynamics simulations of low-altitude and near-wall flights based on in vivo high-speed kinematic measurements to quantify changes in the self-generated pressure and velocity cues at the sensitive mechanosensory antennae. We validated the principle that encoding aerodynamic information can enable collision avoidance by developing a quadcopter with a sensory system inspired by the mosquito. Such low-power sensing systems have major potential for future use in safer rotorcraft control systems

Topics in construction safety and health : struck-by and caught-in hazards : an interdisciplinary annotated bibliography

"These referenced articles provide literature on the dangers to construction workers from job hazards in their occupations including the equipment they use and the type of work environment they are working in" - NIOSHTIC-2NIOSHTIC no. 20068258Production of this document was supported by cooperative agreement OH 009762 from the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH.Struck-by-and-Caught-in-Hazards-annotated-bibliography.pdfcooperative agreement OH 009762 from the National Institute for Occupational Safety and Healt