[2603.02245] LMU-Based Sequential Learning and Posterior Ensemble Fusion for Cross-Domain Infant Cry Classification

Electrical Engineering and Systems Science > Audio and Speech Processing arXiv:2603.02245 (eess) [Submitted on 24 Feb 2026] Title:LMU-Based Sequential Learning and Posterior Ensemble Fusion for Cross-Domain Infant Cry Classification Authors:Niloofar Jazaeri, Hilmi R. Dajani, Marco Janeczek, Martin Bouchard View a PDF of the paper titled LMU-Based Sequential Learning and Posterior Ensemble Fusion for Cross-Domain Infant Cry Classification, by Niloofar Jazaeri and 3 other authors View PDF Abstract:Decoding infant cry causes remains challenging for healthcare monitoring due to short nonstationary signals, limited annotations, and strong domain shifts across infants and datasets. We propose a compact acoustic framework that fuses MFCC, STFT, and pitch features within a multi-branch CNN encoder and models temporal dynamics using an enhanced Legendre Memory Unit (LMU). Compared to LSTMs, the LMU backbone provides stable sequence modeling with substantially fewer recurrent parameters, supporting efficient deployment. To improve cross-dataset generalization, we introduce calibrated posterior ensemble fusion with entropy-gated weighting to preserve domain-specific expertise while mitigating dataset bias. Experiments on Baby2020 and Baby Crying demonstrate improved macro-F1 under cross-domain evaluation, along with leakageaware splits and real-time feasibility for on-device monitoring. Comments: Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)...