Domain-Agnostic Causal-Aware Audio Transformer for Infant Cry Classification

• URL: http://arxiv.org/abs/2512.16271v1
• Date: Thu, 18 Dec 2025 07:40:44 GMT
• Title: Domain-Agnostic Causal-Aware Audio Transformer for Infant Cry Classification
• Authors: Geofrey Owino, Bernard Shibwabo Kasamani, Ahmed M. Abdelmoniem, Edem Wornyo,
• Abstract summary: We propose DACH-TIC, a Domain-Agnostic Causal-Aware Hierarchical Audio Transformer for robust infant cry classification.<n>The model integrates causal attention, hierarchical representation learning, multi-task supervision, and adversarial domain generalization within a unified framework.<n>The model generalizes effectively to unseen acoustic environments, with a domain performance gap of only 2.4 percent.
• Score: 5.764453198495989
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and interpretable classification of infant cry paralinguistics is essential for early detection of neonatal distress and clinical decision support. However, many existing deep learning methods rely on correlation-driven acoustic representations, which makes them vulnerable to noise, spurious cues, and domain shifts across recording environments. We propose DACH-TIC, a Domain-Agnostic Causal-Aware Hierarchical Audio Transformer for robust infant cry classification. The model integrates causal attention, hierarchical representation learning, multi-task supervision, and adversarial domain generalization within a unified framework. DACH-TIC employs a structured transformer backbone with local token-level and global semantic encoders, augmented by causal attention masking and controlled perturbation training to approximate counterfactual acoustic variations. A domain-adversarial objective promotes environment-invariant representations, while multi-task learning jointly optimizes cry type recognition, distress intensity estimation, and causal relevance prediction. The model is evaluated on the Baby Chillanto and Donate-a-Cry datasets, with ESC-50 environmental noise overlays for domain augmentation. Experimental results show that DACH-TIC outperforms state-of-the-art baselines, including HTS-AT and SE-ResNet Transformer, achieving improvements of 2.6 percent in accuracy and 2.2 points in macro-F1 score, alongside enhanced causal fidelity. The model generalizes effectively to unseen acoustic environments, with a domain performance gap of only 2.4 percent, demonstrating its suitability for real-world neonatal acoustic monitoring systems.

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