Mario Lasseck

Wissenschaftler*in

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Bereich:
Zukunft der Sammlung

Ich forsche zu Deep-Learning-Methoden für die Artenerkennung, wobei mein Schwerpunkt auf der akustischen Identifizierung von Tieren liegt.

Projekt(e)

Publikationen

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.17571189Open Access

Lasseck, M. (2025). Towards Improved Species Identification. Abstract Book [IBAC 25], 7 [S2-1]. URL: https://www.ibac25.com/post/abstract-bookOpen Access

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.pdfOpen Access

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.e119534Open Access

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.pdfOpen Access

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/s10336-022-02018-8Open Access

Jäckel, D., Mortega, K.G., Darwin, S., Brockmeyer, U., Sturm, U., Lasseck, M., Moczek, N., Lehmann, G.U.C., Voigt-Heucke, S.L. (2022). Community engagement and data quality: best practices and lessons learned from a citizen science project on birdsong. Journal of Ornithology, 164, 233–244. DOI: https://doi.org/10.1007/s10336-022-02018-8Open Access

Wägele, J., Bodesheim, P., Bourlat, S.J., Denzler, J., Diepenbroek, M., Fonseca, V., Frommolt, K.H., Geiger, M.F., Gemeinholzer, B., Glöckner, F.O., Haucke, T., Kirse, A., Kölpin, A., Kostadinov, I., Kühl, H.S., Kurth, F., Lasseck, M., Liedke, S., Losch, F., Müller, S., Petrovskaya, N., Piotrowski, K., Radig, B., Scherber, C., Schoppmann, L., Schulz, J., Steinhage, V., Tschan, G.F., Vautz, W., Velotto, D., Weigend, M., Wildermann, S. (2022). Towards a multisensor station for automated biodiversity monitoring. Basic and Applied Ecology, 59, 105-138. DOI: https://doi.org/10.1016/j.baae.2022.01.003Open Access

Stehle, M., Lasseck, M., Khorramshahi, O., Sturm, U. (2020). Evaluation of acoustic pattern recognition of nightingale (Luscinia megarhynchos) recordings by citizens. Research Ideas and Outcomes, 6, e50233. DOI: https://doi.org/10.3897/rio.6.e50233Open Access

Lasseck, M. (2019). Bird Species Identification in Soundscapes. CEUR Workshop Proceedings, 2380. URL: https://ceur-ws.org/Vol-2380/paper_86.pdfOpen Access