Antonella Torrisi

Christopher Mitcheltree, Vincent Lostanlen, Emmanouil Benetos, Mathieu Lagrange: SCRAPL: Scattering Transform with Random Paths for Machine Learning https://arxiv.org/abs/2602.11145 https://arxiv.org/pdf/2602.11145 https://arxiv.org/html/2602.11145

A) Dendrogram of the development dataset showing the clustering structure and optimal cut points, and spectrograms of representative calls extracted from cluster 0 and cluster 1. Within the main clusters, we observed further branching; B) UMAP projection divided into 𝐾 = 2 clusters using HAC.

Our new pre-print shows how unsupervised clustering methods can identify biologically meaningful differences in early vocal production, with no human feedback. @antorrisi.bsky.social
has led this interdisciplinary collaboration based on computational methods + #chicks 🐣 arxiv.org/abs/2601.12203

Last ASAB conference of my PhD journey! 🐣
Presenting my poster “Vocal Echo: a closed-loop system for real-time vocal analysis of domestic chicks” at #ASABWinter2025
Come by the poster session to chat and check out the demo via the link below!
@elisabettaversace.bsky.social @emmanouilb.bsky.social

Touching Without Contact: We Physically Sense Objects Before Feeling Them - Neuroscience News A new study shows that humans possess a form of “remote touch,” allowing them to detect hidden objects in sand before making direct contact.

Our new study on remote touch .....✋
Touching Without Contact: We Physically Sense Objects Before Feeling Them - neurosciencenews.com/remote-touch... @zhengqichen.bsky.social @lauracrucianelli.bsky.social @elisabettaversace.bsky.social #LorenzoJamone ✋
--> 📹 youtu.be/6hpuLojesyQ?...

Very excited and proud to share my postdoctoral research with @neurrriot.bsky.social looking at the context-specific encoding of social behavior 💃🕺 in hormone-sensitive, large-scale brain networks in mice!

www.biorxiv.org/content/10.1...

#neuroskyence #compneurosky 🧪
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Opinion | I’m a Genocide Scholar. I Know It When I See It.

"I’m a Genocide Scholar. I Know It When I See It." www.nytimes.com/2025/07/15/o...

Image with the name of the paper "REFINING MUSIC SAMPLE IDENTIFICATION WITH A SELF-SUPERVISED GRAPH NEURAL NETWORK", names of the authors (Aditya Bhattacharjee, Ivan Meresman Higgs, Mark Sandler, and Emmanouil Benetos from Queen Mary University of London, UK), and a figure with the illustrated ASID methodology (2 stages): (A) Given a query, we compute segment-level embeddings (fingerprints), matched to reference embeddings via approximate nearest-neighbour (ANN) search; based on which, candidate songs are retrieved from the reference database through a lookup process (dotted arrows). (B) A multi-head cross-attention (MHCA) classifier refines and ranks candidates using node embedding matrices NMq (query) and NMr (references).

Our paper on automatic sample identification (arxiv.org/abs/2506.14684) was accepted at @ismir_conf 2025! 🎵🎶
We propose an architecture that can detect music samples that have been reused in new compositions, even after pitch-shifting, time-stretching, and other transformations!🧵

The value of ecologically irrelevant animal cognition research

Opinion by Scarlett Howard
tinyurl.com/37aedmfx

I love this work from my colleagues from the psychology department!

Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors In computational bioacoustics, deep learning models are composed of feature extractors and classifiers. The feature extractors generate vector representations of the input sound segments, called embeddings, which can be input to a classifier. While benchmarking of classification scores provides insights into specific performance statistics, it is limited to species that are included in the models' training data. Furthermore, it makes it impossible to compare models trained on very different taxonomic groups. This paper aims to address this gap by analyzing the embeddings generated by the feature extractors of 15 bioacoustic models spanning a wide range of setups (model architectures, training data, training paradigms). We evaluated and compared different ways in which models structure embedding spaces through clustering and kNN classification, which allows us to focus our comparison on feature extractors independent of their classifiers. We believe that this approach lets us evaluate the adaptability and generalization potential of models going beyond the classes they were trained on.

Preprint from us: "Clustering and novel class recognition: evaluating bioacoustic deep learning feature extractors" https://arxiv.org/abs/2504.06710 -- Vincent Kather evaluates a big set of deep embeddings for #bioacoustics

Alex and Robyn had an amazing day engaging the children of Wapping High School in the fascinating world of animal behaviour! @asabeducation.bsky.social #nationalscienceweek #animalbehaviour #outreach