# Infant Cry Detection Using Causal Temporal Representation

arXiv: https://arxiv.org/abs/2503.06247 · HTML: https://mlsanity.com/paper/2503.06247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection](https://mlsanity.com/paper/2203.15296/) (2022)
- [Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment](https://mlsanity.com/paper/2403.19225/) (2024)
- [Causal Temporal Representation Learning with Nonstationary Sparse Transition](https://mlsanity.com/paper/2409.03142/) (2024)
- [Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms](https://mlsanity.com/paper/1808.03253/) (2018)
- [Towards Causal Representations of Climate Model Data](https://mlsanity.com/paper/2312.02858/) (2023)
- [Infant Crying Detection in Real-World Environments](https://mlsanity.com/paper/2005.07036/) (2020)
- [Pushing the Limit of Sound Event Detection with Multi-Dilated Frequency Dynamic Convolution](https://mlsanity.com/paper/2406.13312/) (2024)

## Abstract (arXiv)

This paper addresses a major challenge in acoustic event detection, in particular infant cry detection in the presence of other sounds and background noises: the lack of precise annotated data. We present two contributions for supervised and unsupervised infant cry detection. The first is an annotated dataset for cry segmentation, which enables supervised models to achieve state-of-the-art performance. Additionally, we propose a novel unsupervised method, Causal Representation Spare Transition Clustering (CRSTC), based on causal temporal representation, which helps address the issue of data scarcity more generally. By integrating the detected cry segments, we significantly improve the performance of downstream infant cry classification, highlighting the potential of this approach for infant care applications.