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

arXiv: https://arxiv.org/abs/2409.19689 · HTML: https://mlsanity.com/paper/2409.19689/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 29 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Model compression via distillation and quantization](https://mlsanity.com/paper/1802.05668/) (2018)
- [Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights](https://mlsanity.com/paper/1702.03044/) (2017)
- [Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want](https://mlsanity.com/paper/2403.20271/) (2024)
- [Audio Set classification with attention model: A probabilistic perspective](https://mlsanity.com/paper/1711.00927/) (2017)
- [Infant Crying Detection in Real-World Environments](https://mlsanity.com/paper/2005.07036/) (2020)
- [Estimating Peer Direct and Indirect Effects in Observational Network Data](https://mlsanity.com/paper/2408.11492/) (2024)
- [Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning](https://mlsanity.com/paper/1603.05824/) (2016)
- [Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks](https://mlsanity.com/paper/2409.08544/) (2024)

## Built on by (most cited first, 4 of 4)

- [Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing](https://mlsanity.com/paper/2410.06190/) (2024)
- [Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation](https://mlsanity.com/paper/2410.15620/) (2024)
- [Contextualized Token Discrimination for Speech Search Query Correction](https://mlsanity.com/paper/2509.04393/) (2025)
- [Proactive Dialogue Model with Intent Prediction](https://mlsanity.com/paper/2604.27379/) (2026)

## Abstract (arXiv)

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.