Charles Onu

CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds

David Budaghyan

Arsenii Gorin

This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is … (see more)a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.

2024-04-14

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

doi.org

arxiv.org

Learning domain-invariant classifiers for infant cry sounds

Charles Onu

Hemanth K. Sheetha

Arsenii Gorin

Doina Precup

2023-11-30

ArXiv (preprint)

arxiv.org

A cry for help: Early detection of brain injury in newborns

Charles Onu

Samantha Latremouille

Arsenii Gorin

Junhao Wang

Uchenna Ekwochi

P. Ubuane

O. Kehinde

Muhammad A. Salisu

Datonye Briggs

Yoshua Bengio

Doina Precup

2023-10-12

ArXiv (preprint)

doi.org

arxiv.org

Self-Supervised Learning for Infant Cry Analysis

Arsenii Gorin

Cem Subakan

Sajjad Abdoli

Junhao Wang

Samantha Latremouille

Charles Onu

In this paper, we explore self-supervised learning (SSL) for analyzing a first-of-its-kind database of cry recordings containing clinical in… (see more)dications of more than a thousand newborns. Specifically, we target cry-based detection of neurological injury as well as identification of cry triggers such as pain, hunger, and discomfort. Annotating a large database in the medical setting is expensive and timeconsuming, typically requiring the collaboration of several experts over years. Leveraging large amounts of unlabeled audio data to learn useful representations can lower the cost of building robust models and, ultimately, clinical solutions. In this work, we experiment with self-supervised pre-training of a convolutional neural network on large audio datasets. We show that pre-training with SSL contrastive loss (SimCLR) performs significantly better than supervised pre-training for both neuro injury and cry triggers. In addition, we demonstrate further performance gains through SSL-based domain adaptation using unlabeled infant cries. We also show that using such SSL-based pre-training for adaptation to cry sounds decreases the need for labeled data of the overall system.

2023-06-04

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (published)

doi.org

arxiv.org

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study

Lara Kanbar

Wissam Shalish

Charles Onu

Samantha Latremouille

Lajos Kovacs

Martin Keszler

Sanjay Chawla