Initial assumptions for the system of automatic detection and classification of aircraft noise events

• Autorzy: Bukała Michał , Nowoświat Artur , Chyla Andrzej

Identyfikatory

DOI

10.21008/j.0860-6897.2022.2.08

• Języki publikacji: EN

Abstrakty

EN

The present study undertakes the development and implementation of an algorithm for an automatic separation of acoustic events related to aircraft flights. The data are provided by noise monitoring stations operating as part of multi-point continuous noise measurement systems around small and medium-sized airports and helicopter landing sites in Poland. The article presents initial assumptions of the developed method based on the conclusions of the research. For this purpose, two different methods of airborne noise signal detection will be discussed. The first method is based on the analysis of the value of the changing rate of the signal being the difference between the value of the analysed sample and the value of the h-th previous sample of the recorded sound level time history. The second method uses a convolutional neural network operating on values recorded in 1/3-octave bands. The objective of the study is to examine the effectiveness and limitations of the selected methods on the collected representative input data.

Słowa kluczowe

EN

aircraft noise; noise monitoring; noise detection

PL

hałas lotniczy; monitoring hałasu; wykrywanie hałasu

• Wydawca: Poznan University of Technology. Institute of Applied Mechanics
• Czasopismo: Vibrations in Physical Systems
• Rocznik: 2022
• Tom: Vol. 33, nr 2
• Strony: art. no. 2022208
• Opis fizyczny: Bibliogr. 21 poz., il. kolor., wykr.

Twórcy

autor

Bukała Michał

• Silesian University of Technology

• https://orcid.org/0000-0002-4172-7754

autor

Nowoświat Artur

• Silesian University of Technology

• https://orcid.org/0000-0002-0277-7388

autor

Chyla Andrzej

• SVANTEK Sp. z o.o.

• https://orcid.org/0000-0001-6647-2375

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).