Drittmittelprojekt
Integration (bio-)akustischer Methoden zur Quantifizierung biologischer Vielfalt in das Waldmonitoring
Machbarkeitsstudie AkWamo
Mario Lasseck
Wissenschaftler:in
Projekt(e)
Publikationen
Lasseck, M. (2023). Bird Species Recognition using Convolutional Neural Networks with Attention on Frequency Bands. CEUR Workshop Proceedings, 1-9. URL: https://ceur-ws.org/Vol-3497/paper-175.pdf
Sturm, U., Mortega, K.G., Jäckel, D., Darwin, S., Brockmeyer, U., Lasseck, M., Moczek, N., Lehmann, G.U.C., Voigt-Heucke, S.L. (2023). Community engagement and data quality: best practices and lessons learned from a citizen science project on birdsong. Journal of Ornithology, 164(1), 233-244. DOI: https://doi.org/10.1007/s10336-022-02018-8
Lasseck, M. (2024). Improving Bird Recognition using Pseudo-Labeled Recordings from the Target Location. Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024), 3740, 1-9. URL: https://ceur-ws.org/Vol-3740/paper-199.pdf
Wolfgang Wägele, J., Tschan, G.F., Werner, B., Jahn, O., Lasseck, M., Frommolt, K.H. (2024). Bioacoustic data acquisition and species recognition. In Weather stations for biodiversity: a comprehensive approach to an automated and modular monitoring system (119-157). Pensoft Publishers. DOI: https://doi.org/10.3897/ab.e119534
Lasseck, M. (2025). Towards Improved Species Identification. Abstract Book [IBAC 25], 7 [S2-1]. URL: https://www.ibac25.com/post/abstract-book
Lasseck, M., Eibl, M., Klinck, H., Kahl, S. (2026). BirdNET+ V3.0 model developer preview (Version Number: Preview 3) [Software]. Museum für Naturkunde (MfN) - Leibniz-Institut für Evolutions- und Biodiversitätsforschung. DOI: https://doi.org/10.5281/ZENODO.17571189