IEEE WCCI 2026 Special Session

Topics of Interests

• Machine learning for large-scale bioacoustic data
• Automated detection and classification of species
• Population size estimation from acoustic or multimodal data
• Novel embeddings and representations for ecological data
• Deep learning for biodiversity monitoring and conservation
• Integration of audio, visual, and sensor modalities
• Case studies from zoos, field stations, and citizen science projects
• Tools, datasets, and reproducibility in ecological AI research

Novelty and Motivation

The biodiversity crisis continues to grow and becomes more visible every year. Although much monitoring is already conducted, there is a massive information gap due to the scale of the issue: for example there is currently ongoing discussion about whether the recently-identified “insect apocalypse” applies across all species and all parts of the world. Resolving these issues is of vital importance since insects and many other animals are, among other things, crucial to society as crop pollinators. On the positive side, new information streams for biodiversity are becoming available, from audio and video recorders, satellite and drone imaging, and many other environmental sensors.
Signal processing and statistical optimisation have a key role to play, since they are needed to turn these raw data streams into evidence.

Scientific development of such methods requires attention to the specific properties of the signals as well as the inferences required: for example, a single audio signal may embed evidence of multiple different species and their interactions, as well as weather and human factors.