skip to main content
10.1145/3561212.3561217acmotherconferencesArticle/Chapter ViewAbstractPublication PagesamConference Proceedingsconference-collections
short-paper

Exploring a Long-term Dataset of Nature Reserve Ambisonics Recordings

Authors Info & Claims
Published:10 October 2022Publication History

ABSTRACT

Since 2017, monthly 3D audio recordings of a nature preserve capture the acoustic environment over seasons and years. The recordings are made at the same location and using the same recording equipment, capturing one hour before and after sunset. The recordings, annotated with real-time weather data and manually labeled for acoustic events, are made to understand if and how a natural soundscape evolves over time allowing for data-driven speculation about transformations of the soundscape that might be caused by climate change. After a short description of the general project and its current state, methods and results of algorithmic analysis used are presented and the results are discussed. Further methods of collecting additional data and expanded analyses of the body of data are suggested.

References

  1. 2022. Arbimon. https://arbimon.rfcx.org/Google ScholarGoogle Scholar
  2. 2022. BirdNET. https://birdnet.cornell.edu/Google ScholarGoogle Scholar
  3. 2022. Center for Global Soundscapes. https://centerforglobalsoundscapes.org/Google ScholarGoogle Scholar
  4. 2022. Cornell Lab of Ornithology. https://www.birds.cornell.edu/homeGoogle ScholarGoogle Scholar
  5. 2022. Rainforest Connection. https://rfcx.org/Google ScholarGoogle Scholar
  6. 2022. Raven Sound Software. https://ravensoundsoftware.com/Google ScholarGoogle Scholar
  7. 2022. Urban Soundscapes of the World. https://urban-soundscapes.org/Google ScholarGoogle Scholar
  8. 2022. Vogelfreistaette Flachwasser. https://www.ecolisten.org/locations/Vogelfreistaette_Flachwasser/Google ScholarGoogle Scholar
  9. Carlos Corrada Bravo, Rafael Berríos, and T. Mitchell Aide. 2017. Species-specific audio detection: A comparison of three template-based detection algorithms using random forests. PeerJ Computer Science 3 (04 2017), e113. https://doi.org/10.7717/peerj-cs.113Google ScholarGoogle Scholar
  10. Stefan Kahl, Connor Wood, Maximilian Eibl, and Holger Klinck. 2021. BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics 61 (01 2021), 101236. https://doi.org/10.1016/j.ecoinf.2021.101236Google ScholarGoogle Scholar
  11. Jian Kang and Francesco Aletta. 2018. The Impact and Outreach of Soundscape Research. Environments 5 (05 2018), 58–68. https://doi.org/10.3390/environments5050058Google ScholarGoogle Scholar
  12. Jian Kang, Kalliopi Chourmouziadou, Konstantinos Sakantamis, Bo Wang, and Yijyng Hao (Eds.). 2013. Soundscape of European Cities and Landscapes. COST TUD Action TD-0804; Soundscape COST, Oxford, UK. http: //soundscape-cost.org/documents/COST_TD0804_E-book_2013.pdfGoogle ScholarGoogle Scholar
  13. Qiankun Liu, Zhong Liu, Jingang Jiang, and Jiaguo Qi. 2020. A new soundscape analysis tool: Soundscape Analysis and Mapping System (SAMS). Applied Acoustics 169 (12 2020), 107454. https://doi.org/10.1016/j.apacoust.2020.107454Google ScholarGoogle Scholar
  14. Pawel Malecki, Agnieszka Ozga, and Janusz Piechowicz. 2016. Soundscape analysis based on ambisonic recordings executed in a primeval forest Soundscape analysis based on ambisonic recordings executed in a primeval forest. In Proceedings of the International Congress on Acoustics 2016. Buenos Aires, Argentinia.Google ScholarGoogle Scholar
  15. Andrew Mitchell, Tin Oberman, Francesco Aletta, Mercede Erfanian, Magdalena Kachlicka, Matteo Lionello, and Jian Kang. 2021. The International Soundscape Database: An integrated multimedia database of urban soundscape surveys – questionnaires with acoustical and contextual information. https://www.zenodo.org/record/5914715#.YnwwoGDP00QGoogle ScholarGoogle Scholar
  16. Veronica Morfi, Ines Nolasco, Vincent Lostanlen, Shubhr Singh, Ariana Strandburg-Peshkin, Lisa Gill, Hanna Pamuła, David Benvent, and Dan Stowell. 2021. Few-Shot Bioacoustic Event Detection: A New Task at the DCASE 2021 Challenge. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021). Barcelona, Spain, 145–149.Google ScholarGoogle Scholar
  17. Garth Paine. 2018. AELab Tagging Instructions. http://acousticecologylab.org/wp-content/uploads/2017/01/AELabTaggingHowTo.pdfGoogle ScholarGoogle Scholar
  18. Garth Paine, Leah Barclay, Sabine Feisst, and Daniel Gilfillan. 2015. The Listen(n) Project: Acoustic Ecology as a Tool for Remediating Environmental Awareness. In Proceedings of the 21st International Symposium on Electronic Art. Vancouver, Canada.Google ScholarGoogle Scholar
  19. R. Murray Schafer. 1977. The Tuning of the World. Knopf.Google ScholarGoogle Scholar
  20. Michael Frank Southworth. 1967. The Sonic Environment of Cities. Environment and Behavior 1 (1967), 49 – 70.Google ScholarGoogle Scholar
  21. Dan Stowell. 2022. Computational bioacoustics with deep learning: a review and roadmap. PEERJ (21 March 2022). https://doi.org/10.7717/peerj.13152Google ScholarGoogle Scholar
  22. Luca Turchet, Gyorgy Fazekas, Mathieu Lagrange, Hossein Ghadikolaei, and Carlo Fischione. 2020. The Internet of Audio Things: State of the Art, Vision, and Challenges. IEEE Internet of Things Journal PP (05 2020), 1–1. https://doi.org/10.1109/JIOT.2020.2997047Google ScholarGoogle Scholar
  23. Juan Ulloa, Sylvain Haupert, Juan Latorre Gil, Thierry Aubin, and Jerome Sueur. 2021. Scikit-maad: An open-source and modular toolbox for quantitative analysis in Python. Methods in Ecology and Evolution 12 (08 2021). https://doi.org/10.1111/2041-210X.13711Google ScholarGoogle Scholar
  24. Dylan Wilford, Jennifer Miksis-Olds, Bruce Martin, and Kim Lowell. 2021. Introduction and application of a proposed method for quantitative soundscape analysis: The soundscape code. The Journal of the Acoustical Society of America 149 (04 2021), A72–A72. https://doi.org/10.1121/10.0004555Google ScholarGoogle Scholar

Index Terms

  1. Exploring a Long-term Dataset of Nature Reserve Ambisonics Recordings

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        AM '22: Proceedings of the 17th International Audio Mostly Conference
        September 2022
        245 pages
        ISBN:9781450397018
        DOI:10.1145/3561212

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 10 October 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • short-paper
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate177of275submissions,64%
      • Article Metrics

        • Downloads (Last 12 months)19
        • Downloads (Last 6 weeks)4

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format