Abstract : The study of ecosystems relies mainly on surveying the species present in a given area and monitoring the evolution of their populations. Ecoacoustics [Stowell and Sueur, 2020] offers an innovative approach by using microphones placed in natural environments to periodically record the soundscape. This data acquisition, which can span from a single day to several years, provides a rich foundation for analyzing biodiversity and ecosystem spatio-temporal dynamics.
Traditionally, experts analyze these recordings by identifying sounds of interest based on temporal patterns and frequency content. More recently, the rise of neural models for species classification has opened new perspectives for interpreting soundscapes [Michaud, 2025]. However, these approaches have two major limitations: conventional methods lack the precision required for fine-grained soundscape analysis, while neural models, despite their eciency, remain species-centered and inherently dicult for humans to interpret. What’s more, the learning of complex models is hampered by the scarcity of annotated data, limiting the possibility of model exploitation. In this context, the objective of this thesis will be to develop interpretable and resource-efficient approaches based on automatic audio signal processing for the improvement and monitoring of ecosystems through acoustics.
Objective : Initially, the focus will be on analyzing and simulating ecoacoustic data to train segmentation and individual clustering models. These fine-temporal-resolution approaches may draw inspiration from recent work in speaker diarization, such as EEND-VC [Kinoshita et al., 2021]. This will enable the longitudinal prediction of biodiversity indicators such as species richness (the number of different species) and abundance (the number of individuals) [Bradfer-Lawrence et al., 2023]. Second, we plan to condition the models on environmental measures that significantly impact species song (temperature, humidity, season, time of day). Recent techniques, developed for instance for disentanglement, may be explored [Wang et al., 2024; Almudevar et al., 2024]. Finally, to improve our understanding of the models, they will need to provide audible explanations in the form of prototypes (sonotypes) [Paissan et al., 2024; Mariotte et al., 2024].
Funding and location : This thesis is funded by the EcoSound-XI ANR project in collaboration between the Laboratoire d’Informatique de l’Université du Mans (LIUM) and the CESCO laboratory at the National Natural History Museum in Paris (MNHN). The thesis will take place at the LIUM in Le Mans and periodic meetings will be organized with the researchers from CESCO.
Qualifications: The candidate must have expertise in machine learning (particularly deep learning) and signal processing.
Applications (CV, motivation letter, Master transcripts) should be submitted to the official platform of the doctoral school before 10th of May :
https://amethis.doctorat.org/amethis-client/prd/consulter/offre/3325
Information and contact : theo.mariotte@univ-lemans.fr and marie.tahon@univ-lemans.fr
References
- K. Kinoshita, T. von Neumann, M. Delcroix, C. Boeddeker, et Reinhold Haeb-Umbach. Utterance-by-Utterance Overlap-Aware Neural Diarization with Graph-PIT. 2022. http://arxiv.org/abs/2207.13888.
- F. Paissan, M. Ravanelli, et C. Subakan. Listenable Maps for Audio Classifiers. ICML 2024. https://doi.org/10.48550/arXiv.2403.13086.
- F. Michaud, Data-centric artificial intelligence for ecoacoustics. PhD thesis, Sorbonne University, 2025
- T. Mariotte, A. Almudévar, M. Tahon, et A. Ortega. AN EXPLAINABLE PROXY MODEL FOR MULTILABEL AUDIO SEGMENTATION. ICASSP 2024. https://univ-lemans.hal.science/hal-04393946.
- D. Stowell and J. Sueur, Ecoacoustics: acoustic sensing for biodiversity monitoring at scale, Remote Sensing in Ecology and Conservation, vol. 6, no. 3, pp. 217–219, 2020.
- X. Wang, H. Chen, S. Tang, Z. Wu, and W. Zhu, Disentangled representation learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 9677–9696, 2024.
- T. Bradfer-Lawrence, C. Desjonqueres, A. Eldridge, A. Johnston, and O. Metcalf, Using acoustic indices in ecology: Guidance on study design, analyses and interpretation, Methods in Ecology and Evolution, vol. 14, no. 9, pp. 2192–2204, 2023.
