2 citations · 3 across the 5 of their papers we have counts for
5 papers
Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answering
Dinh Phu Tran, Jihoon Jeong, Saad Wazir +4
We present a formal problem formulation for \textit{Reliable} Audio-Visual Question Answering (-AVQA), where we prefer abstention over answering incorrectly. While rec…
Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception
Mathis Rezzouk, Fabrice Gagnon, Alyson Champagne +4
EEG-based analysis of pain perception, enhanced by machine learning, reveals how the brain encodes pain by identifying neural patterns evoked by noxious stimulation. However, a maj…
Planing It by Ear: Convolutional Neural Networks for Acoustic Anomaly Detection in Industrial Wood Planers
Anthony Deschênes, Rémi Georges, Cem Subakan +3
In recent years, the wood product industry has been facing a skilled labor shortage. The result is more frequent sudden failures, resulting in additional costs for these companies…
Self-supervised learning for infant cry analysis
Arsenii Gorin, Cem Subakan, Sajjad Abdoli +3
In this paper, we explore self-supervised learning (SSL) for analyzing a first-of-its-kind database of cry recordings containing clinical indications of more than a thousand newbor…
CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds
David Budaghyan, Charles C. Onu, Arsenii Gorin +2
This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is a public speaker verification challenge…