Privacy-Enhancing Infant Cry Classification with Federated Transformers and Denoising Regularization
Abstract
Infant cry classification can aid early assessment of infant needs. However, deployment of such solutions is limited by privacy concerns around audio data, sensitivity to background noise, and domain shift across recording environments. We present an end-to-end infant cry analysis pipeline that integrates a denoising autoencoder (DAE), a convolutional tokenizer, and a Transformer encoder trained using communication-efficient federated learning (FL). The system performs on-device denoising, adaptive segmentation, post hoc calibration, and energy-based out-of-distribution (OOD) abstention. Federated training employs a regularized control variate update with 8-bit adapter deltas under secure aggregation. Using the Baby Chillanto and Donate-a-Cry datasets with ESC-50 noise overlays, the model achieves a macro F1 score of 0.938, an AUC of 0.962, and an Expected Calibration Error (ECE) of 0.032, while reducing per-round client upload from approximately 36 to 42 MB to 3.3 MB. Real-time edge inference on an NVIDIA Jetson Nano (4 GB, TensorRT FP16) achieves 96 ms per one-second spectrogram frame. These results demonstrate a practical path toward privacy-preserving, noise-robust, and communication-efficient infant cry classification suitable for federated deployment.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper introduces a novel federated learning framework for infant cry classification that effectively addresses privacy and noise robustness. The integration of advanced techniques such as denoising autoencoders and communication-efficient training protocols represents a meaningful contribution to the field of machine learning and healthcare applications.
Methodology
The paper presents a comprehensive methodology that integrates a denoising autoencoder (DAE), a convolutional tokenizer, and a Transformer encoder within a federated learning framework. The use of DAE for on-device denoising is particularly innovative, addressing the challenges of background noise and domain shift effectively. The communication-efficient federated learning approach, utilizing control variates and secure aggregation, is well-structured and demonstrates a clear understanding of the constraints in federated settings. The multi-term training objective is thoughtfully designed to enhance classification performance while ensuring reliability through calibration and out-of-distribution detection.
Results & evaluation
The experimental setup is robust, utilizing two relevant datasets (Baby Chillanto and Donate-a-Cry) with simulated noise conditions to evaluate the model's performance. The reported metrics, including macro-F1, AUC, and Expected Calibration Error (ECE), indicate strong performance, particularly under challenging conditions such as low signal-to-noise ratios. The statistical significance testing and ablation studies enhance the credibility of the results, showcasing the effectiveness of the proposed methods compared to established baselines.
Reproducibility
The paper provides sufficient implementation details, including the architecture of the models, training parameters, and evaluation protocols. However, the lack of specific URLs for code or demo makes it challenging to fully assess reproducibility. Future work should consider releasing the codebase to facilitate validation and further research.
Limitations
The study relies on public datasets and simulated noise, which may not fully represent real-world scenarios. Additionally, the generalizability of the findings to diverse environments and populations remains uncertain. The authors acknowledge the need for prospective multi-site studies to validate their approach in practical settings.
Broader impact
The proposed system has significant implications for healthcare, particularly in neonatal care, where timely and accurate assessment of infant needs can improve outcomes. The privacy-preserving aspects of the federated learning approach are crucial in sensitive applications involving audio data. The methodology could be extended to other domains requiring similar privacy and robustness considerations. This paper introduces a novel federated learning framework for infant cry classification that effectively addresses privacy and noise robustness. The integration of advanced techniques such as denoising autoencoders and communication-efficient training protocols represents a meaningful contribution to the field of machine learning and healthcare applications.
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