Feasibility of Discriminating UAV Propellers Noise from Distress Signals to Locate People in Enclosed Environments Using MEMS Microphone Arrays Detecting and finding people are complex tasks when visibility is reduced. This happens, for example, if a fire occurs. In these situations, heat sources and large amounts of smoke are generated. Under these circumstances, locating survivors using thermal or conventional cameras is not possible and it is necessary to use alternative techniques. The challenge of this work was to analyze if it is feasible the integration of an acoustic camera, developed at the University of Valladolid, on an unmanned aerial vehicle (UAV) to locate, by sound, people who are calling for help, in enclosed environments with reduced visibility. The acoustic array, based on MEMS (microelectromechanical system) microphones, locates acoustic sources in space, and the UAV navigates autonomously by closed enclosures. This paper presents the first experimental results locating the angles of arrival of multiple sound sources, including the cries for help of a person, in an enclosed environment. The results are promising, as the system proves able to discriminate the noise generated by the propellers of the UAV, at the same time it identifies the angles of arrival of the direct sound signal and its first echoes reflected on the reflective surfaces.

Audiovisual sensing from a quadcopter: dataset and baselines for source localization and sound enhancement We present an audiovisual dataset recorded outdoors from a quadcopter and discuss baseline results for multiple applications. The dataset includes a scenario for source localization and sound enhancement with up to two static sources, and a scenario for source localization and tracking with a moving sound source. These sensing tasks are made challenging by the strong and timevarying egonoise generated by the rotating motors and propellers. The dataset was collected using a small circular array with 8 microphones and a camera mounted on the quadcopter. The camera view was used to facilitate the annotation of the soundsource positions and can also be used for multimodal sensing tasks. We discuss the audiovisual calibration procedure that is needed to generate the annotation for the dataset, which we make available to the research community1.1http:cis.eecs.qmul.ac.ukprojectsavq