Item Infomation
Title: | A self-training automatic infant-cry detector |
Authors: | Gianpaolo, Coro Serena, Bardelli Armando, Cuttano |
Issue Date: | 2023 |
Publisher: | Springer |
Abstract: | Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). |
Description: | CC BY |
URI: | https://link.springer.com/article/10.1007/s00521-022-08129-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/7333 |
Appears in Collections | OER - Công nghệ thông tin |
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