Infant Cry Detection Using Causal Temporal Representation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916647094190080 |
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| author | Fu, Minghao Li, Danning Gadhiya, Aryan Lambright, Benjamin Alowais, Mohamed Bahnassy, Mohab Elletter, Saad El Dine Toyin, Hawau Olamide Jiang, Haiyan Zhang, Kun Aldarmaki, Hanan |
| author_facet | Fu, Minghao Li, Danning Gadhiya, Aryan Lambright, Benjamin Alowais, Mohamed Bahnassy, Mohab Elletter, Saad El Dine Toyin, Hawau Olamide Jiang, Haiyan Zhang, Kun Aldarmaki, Hanan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06247 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Infant Cry Detection Using Causal Temporal Representation Fu, Minghao Li, Danning Gadhiya, Aryan Lambright, Benjamin Alowais, Mohamed Bahnassy, Mohab Elletter, Saad El Dine Toyin, Hawau Olamide Jiang, Haiyan Zhang, Kun Aldarmaki, Hanan Sound Artificial Intelligence 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. |
| title | Infant Cry Detection Using Causal Temporal Representation |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2503.06247 |