Patent application EP20179531A (EP), filed 2020-06-11
Publication number: EP3751302A1
Title: METHODS AND SYSTEMS FOR ACOUSTIC MACHINE PERCEPTION FOR AN AIRCRAFT
Abstract: [0001]    In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.
IPC codes: G01S  11/    14            A I
G01S  13/    86            A I
G01S  13/   933            A I
G01S  17/    86            A I
G01S  17/   933            A I
G01S   3/    80            A I
Claims: number: 1
text: A method comprising:
number: 2
text: The method of claim 1, wherein causing the array of microphones arranged on the aircraft to acquire the first acoustic data associated with the first object comprises causing one or more microphones arranged on each wing of the aircraft to acquire the first acoustic data associated with the first object.
number: 3
text: The method of any of claims 1-2, wherein the azimuth of the first object, the range of the first object, and the elevation of the first object identified from the first data are relative to the aircraft, the method further comprising:
number: 4
text: The method of any of claims 1-3, further comprising:
number: 5
text: The method of any of claims 1-4, further comprising:
number: 6
text: The method of any of claims 1-5, wherein training the machine learning model comprises training the machine learning model by using one or more weather conditions of the environment identified from the first data as one or more of the ground truth output labels for the machine learning model,
wherein the machine learning model is configured to predict, based on the target acoustic data, that the one or more weather conditions are present in the environment.
number: 7
text: The method of any of claims 1-6, wherein the method is performed while the aircraft is in motion.
number: 8
text: A system comprising:
number: 9
text: The system of claim 8, wherein the computing device is onboard the aircraft.
number: 10
text: The system of any of claims 8-9, wherein the set of operations are performed while the aircraft is in motion.
number: 11
text: The system of any of claims 8-9, wherein the array of microphones comprises one or more microphones arranged on each wing of the aircraft.
number: 12
text: The system of any of claims 8-11, wherein the azimuth of the first object, the range of the first object, and the elevation of the first object identified from the first data are relative to the aircraft, the set of operations further comprising:
number: 13
text: The system of any of claims 8-12, the set of operations further comprising:
number: 14
text: The system of any of claims 8-13, wherein the computing device has a processor and memory storing instructions executable by the processor to perform a set of operations according to the method of any of claims 1-7.
Office action: search_report, dated 2020-12-16
Prior art cited: ref_id: US8059489B1
ref_type: patent
severity: background
ref_id: WO2019067695A1
ref_type: patent
severity: background
ref_id: - SUNGHO JEON ET AL: "Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 20 January 2017 (2017-01-20), XP080750420, DOI: 10.23919/EUSIPCO.2017.8081531
ref_type: npl
severity: background
ref_id: - ANDREA BERNARDINI ET AL: "Drone detection by acoustic signature identification", ELECTRONIC IMAGING, vol. 2017, no. 10, 29 January 2017 (2017-01-29), pages 60 - 64, XP055737394, ISSN: 2470-1173, DOI: 10.2352/ISSN.2470-1173.2017.10.IMAWM-168
ref_type: npl
severity: background
ref_id: US8059489
ref_type: patent
severity: background
ref_id: WO2019067695
ref_type: patent
severity: background
ref_id: - SUNGHO JEON ET AL, "Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20170120), doi:10.23919/EUSIPCO.2017.8081531, XP080750420 [A] 1-14 * the whole document *
ref_type: npl
severity: background
ref_id: - ANDREA BERNARDINI ET AL, "Drone detection by acoustic signature identification", ELECTRONIC IMAGING, (20170129), vol. 2017, no. 10, doi:10.2352/ISSN.2470-1173.2017.10.IMAWM-168, ISSN 2470-1173, pages 60 - 64, XP055737394 [A] 1-14 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2020-12-16)