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As we head into summer, I want to tell you about the bumper crop of new research papers we have from my amazing students and postdocs.

I'm working with a great "decentralised" team of research students/postdocs at Tilburg University, Naturalis Biodiversity Centre, and QMUL.

The papers I'll link here are preprints -- i.e these are the versions we submitted to journals/conferences, and there may be corrections or other edits in the final accepted versions. So here you can read our freshest research news!

• Learning to detect an animal sound from five examples -- This one is a BIG achievement, the first journal-length analysis of a large and long collaboration on "few-shot learning" for animal sound detection. It is led by Ines Nolasco and Shubhr Singh, both PhD students working with me. There's a big set of collaborators behind this one (see the author list), and our success is certainly thanks to them.
• Comparison between transformers and convolutional models for fine-grained classification of insects -- Our first publication on fine-grained image classification, led by Rita Pucci as part of her postdoc work in our EU-funded MAMBO project. Accepted to present at the Camera Traps, AI, and Ecology workshop.
• Feature Embeddings from Large-Scale Acoustic Bird Classifiers Enable Few-Shot Transfer Learning -- This one is from Burooj Ghani, but the work you see here is a new paper from his previous collaboration. There'll be more along these lines very soon.
• Adaptive Representations of Sound for Automatic Insect Recognition -- Led by Marius Faiß, originally as part of his MSc thesis at the University of Leiden, and now developed into a larger project at Naturalis.

The big picture, that you can perhaps see in these paper titles, is that a lot of our focus is on developing machine learning methods for species recognition. There are already plenty of papers and apps for species recognition e.g. of bird sounds, but in order to provide species recognition as a service and a tool at the continental scale there's a lot still to be done. Firstly, going beyond common cases such as birdsong takes some care and attention - I'm happy that we're contributing something to insect sounds (and insect images too) though more work is needed to bring it to the same level of maturity. Second, there are plenty of new developments in deep learning architectures, and still plenty of open questions about the best input representation to use with these, and so there's plenty to explore.