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Infant Cry Detection Using Causal Temporal Representation

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Minghao Fu, Danning Li, Aryan Gadhiya, Benjamin Lambright, Mohamed Alowais, Mohab Bahnassy, Saad El Dine Elletter, Hawau Olamide Toyin
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Haiyan Jiang, Kun Zhang, Hanan Aldarmaki

Sound · Submitted 8 Mar 2025

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 0.94 read the record →

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arXiv ID2503.06247 (version 1)
Pith IDS25NMHK7, Pith's permanent record of this paper and its review
CitationAryan Gadhiya, Benjamin Lambright, Danning Li, Haiyan Jiang, Hanan Aldarmaki, Hawau Olamide Toyin, Kun Zhang, Minghao Fu, Mohab Bahnassy, Mohamed Alowais, Saad El Dine Elletter. "Infant Cry Detection Using Causal Temporal Representation." arXiv:2503.06247 (2025). https://pith.science/paper/S25NMHK7
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,226 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.24 · human participants 0.37 · controlled comparison 0.10 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.54 · mentions code or data availability 0.08.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 0.65 0.08 0.03 asserted 1.00
s1 We present two contributions for supervised and unsupervised infant cry detection. contribution 1.00 0.92 0.05 asserted 0.98
s2 The first is an annotated dataset for cry segmentation, which enables supervised models to achieve state-of-the-art performance. contribution 0.99 0.89 0.09 asserted 1.00
s3 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. contribution 0.89 0.94 0.09 asserted 0.73
s4 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. result 0.66 0.90 0.66 asserted 0.94

What kind of paper the classifier reports this is: method or tool 0.89 · evidence from observation or experiment 0.89.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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