InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries

TL;DR

InfantCryNet is a data-driven framework that leverages pre-trained models, advanced pooling techniques, and model compression to accurately analyze infant cries and their causes, even in noisy environments.

Contribution

The paper introduces InfantCryNet, a novel framework combining pre-trained audio models, statistical and attention pooling, and model compression for infant cry analysis.

Findings

Outperforms state-of-the-art by 4.4% in accuracy

Reduces model size by 7% with no performance loss

Achieves up to 28% size reduction with minimal accuracy decrease

Abstract

Understanding the meaning of infant cries is a significant challenge for young parents in caring for their newborns. The presence of background noise and the lack of labeled data present practical challenges in developing systems that can detect crying and analyze its underlying reasons. In this paper, we present a novel data-driven framework, "InfantCryNet," for accomplishing these tasks. To address the issue of data scarcity, we employ pre-trained audio models to incorporate prior knowledge into our model. We propose the use of statistical pooling and multi-head attention pooling techniques to extract features more effectively. Additionally, knowledge distillation and model quantization are applied to enhance model efficiency and reduce the model size, better supporting industrial deployment in mobile devices. Experiments on real-life datasets demonstrate the superior performance of the proposed framework, outperforming state-of-the-art baselines by 4.4% in classification accuracy. The model compression effectively reduces the model size by 7% without compromising performance and by up to 28% with only an 8% decrease in accuracy, offering practical insights for model selection and system design.