Antonella Torrisi

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...

15.07.2025 17:07 — 👍 1    🔁 1    💬 0    📌 0

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!🧵

25.06.2025 10:45 — 👍 7    🔁 2    💬 1    📌 0

The value of ecologically irrelevant animal cognition research

Opinion by Scarlett Howard
tinyurl.com/37aedmfx

18.06.2025 17:38 — 👍 18    🔁 8    💬 0    📌 0

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

13.06.2025 12:51 — 👍 2    🔁 0    💬 0    📌 0

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

14.04.2025 06:58 — 👍 8    🔁 5    💬 0    📌 0

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

25.03.2025 11:12 — 👍 8    🔁 4    💬 1    📌 0