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.
Abstract (translated)
解码婴儿啼哭的原因对于医疗保健监测来说仍然具有挑战性，这是因为信号短且非平稳、标注有限以及不同婴儿和数据集之间的领域差异较大。我们提出了一种紧凑的声学框架，在多分支CNN编码器中融合了梅尔频率倒谱系数（MFCC）、短时傅立叶变换（STFT）和音高特征，并使用增强型勒让德记忆单元（LMU）来建模时间动态特性。相比于长短期记忆网络（LSTM），LMU骨干架构提供更稳定的序列建模能力，同时大幅减少了递归参数的数量，支持高效部署。 为了提高跨数据集的泛化性能，我们引入了基于熵门控加权的校准后验融合方法，这种方法在保持领域特定专业知识的同时减轻了数据集偏差。我们在Baby2020和婴儿啼哭数据集中进行了实验，在跨域评估中实现了更高的宏平均F1得分，并且通过泄漏感知拆分以及实时可行性验证，展示了设备端监测的应用潜力。
URL
https://arxiv.org/abs/2603.02245
