Karges, Nils und Staab, Jeroen und Rauh, Jürgen und Wegmann, Martin und Taubenböck, Hannes (2022) Soundscapes on edge - The real-time machine learning approach for measuring Soundscapes on resource-constrained devices. In: Proceedings of the 24th International Congress on Acoustics, Seiten 128-139. Proceedings of the 24th International Congress on Acoustics, 2022-10-24 - 2022-10-28, Gyeongju, Korea.
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Offizielle URL: https://ica2022korea.org/data/Proceedings_A14.pdf
Kurzfassung
According to the WHO, noise is a growing problem in urban areas and the second most common environmental cause of health issues in Europe. As a complementary approach to noise maps based on sound pressure levels, soundscape maps can be a useful tool for urban planning, providing more information about how people perceive acoustic environments. This study describes an in-situ soundscape monitoring system based on WASN (Wireless Acoustic Sensor Network) for statistical spatial-temporal prediction of soundscapes in urban areas. Soundscape data on specifically defined spatial scales were observed and evaluated using a microcontroller with a 32-bit nRF52840 Nordic Semiconductors CPU and 1MB of memory in a multifunctional urban area. The use of TinyML enabled machine learning algorithms provided state-of-the-art soundscape classification to a low-cost edge device with extreme resource constraints regarding memory, speed, and lack of GPU support. Sound source types are classified into anthrophony, traffic, biophony, and geophony sounds using ESC-50 for evaluation. Our final MFCC-based CNN achieved an accuracy of 81.6% and even reached higher accuracy in the enclosed studio test. The results show that it is computationally feasible to classify soundscapes on low-power microcontrollers and potentially inform decision-makers based on extended sound analysis
elib-URL des Eintrags: | https://elib.dlr.de/190129/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||
Titel: | Soundscapes on edge - The real-time machine learning approach for measuring Soundscapes on resource-constrained devices | ||||||||||||||||||||||||
Autoren: |
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Datum: | 2022 | ||||||||||||||||||||||||
Erschienen in: | Proceedings of the 24th International Congress on Acoustics | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
Seitenbereich: | Seiten 128-139 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Soundscape, TinyML, WASN | ||||||||||||||||||||||||
Veranstaltungstitel: | Proceedings of the 24th International Congress on Acoustics | ||||||||||||||||||||||||
Veranstaltungsort: | Gyeongju, Korea | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 24 Oktober 2022 | ||||||||||||||||||||||||
Veranstaltungsende: | 28 Oktober 2022 | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Fernerkundung u. Geoforschung, R - Geowissenschaftl. Fernerkundungs- und GIS-Verfahren | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit | ||||||||||||||||||||||||
Hinterlegt von: | Staab, Jeroen | ||||||||||||||||||||||||
Hinterlegt am: | 22 Nov 2022 20:31 | ||||||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:51 |
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