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 CollectionsOER - Công nghệ thông tin
ABSTRACTS VIEWS

76

FULLTEXT VIEWS

29

Files in This Item:
Thumbnail
  • A self-training automatic infant-cry detector-2023.pdf
      Restricted Access
    • Size : 2,22 MB

    • Format : Adobe PDF