Convolutional Neural Networks for Audio-Based Continuous Infant Cry Monitoring at Home

Xie, Jin; Long, Xi; Otte, R.A.; Shan, Caifeng

doi:10.1109/jsen.2021.3123906

Cited by 16 publications

(2 citation statements)

References 33 publications

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“…Tis could assist in the prevention of data loss. Xie et al [21] proposed a two-step approach for detecting continuous infant cries. First, using a volume-based thresholding algorithm, background segments are detected.…”

Section: Single Scenariomentioning

confidence: 99%

Systematic Review of Sleep Monitoring Systems for Babies: Research Issues, Current Status, and Future Challenges

Khan,

Al-Hamid,

Chong

et al. 2024

Health & Social Care in the Community

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Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children’s monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the fundamental practical issues related to daily life are missing. As a result, the safety of babies against suffocation and falling hazards for employed parents or mothers involved in the workforce has been overlooked. Only a few studies exist that cover the age range of 0 months to 4 years, with further focus on the age range of 6 months to 4 years. In response to these gaps in previous research and existing technologies, this review work proposes a vision for a baby safety system that utilizes various sensors and technologies like machine learning and image processing to ensure a safer environment for babies.

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Section: Single Scenariomentioning

confidence: 99%

Systematic Review of Sleep Monitoring Systems for Babies: Research Issues, Current Status, and Future Challenges

Khan,

Al-Hamid,

Chong

et al. 2024

Health & Social Care in the Community

View full text Add to dashboard Cite

show abstract

“…Lavner et al 15 presented logistic regression (LR) and CNN to automatically detect infant cries in a home setting. Xie et al 16 used CNN for audio‐based continuous newborn cry monitoring at home. Mel‐frequency cepstrum coefficients (MFCC), pitch, and formants were taken from the recordings and used to train these algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

CNN‐SCNet: A CNN net‐based deep learning framework for infant cry detection in household setting

Jahangir

2023

Engineering Reports

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Infants are vulnerable to several health problems and cannot express their needs clearly. Whenever they are in a state of urgency and require immediate attention, they cry, which is a form of communication for them. Therefore, the parents of the infants always need to be alert and keep continuous supervision of their infants. However, parents cannot monitor their infants all the time. An infant monitoring system could be a possible solution to monitor the infants, determine when the infants are crying, and notify the parents immediately. Although many such systems are available, most cannot detect infant cries. Some systems have infant cry detection mechanisms, but those mechanisms are not very accurate in detecting infant cries because the mechanisms either include obsolete approaches or machine learning (ML) models that cannot identify infant cries from noisy household settings. To address this limitation, in this research, different conventional and hybrid ML models were developed and analyzed in detail to find out the best model for detecting infant cries in a household setting. A stacked classifier is proposed using different state‐of‐the‐art technologies, outperforming all other developed models. The proposed CNN‐SCNet's (CNN‐Stacked Classifier Network) precision, recall, and f1‐score were found to be 98.72%, 98.05%, and 98.39%, respectively. Infant monitoring systems can use this classifier to detect infant cries in noisy household settings.

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Infant cry classification by using different deep neural network models and hand-crafted features

Özseven

2023

Biomedical Signal Processing and Control

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Convolutional Neural Networks for Audio-Based Continuous Infant Cry Monitoring at Home

Cited by 16 publications

References 33 publications

Systematic Review of Sleep Monitoring Systems for Babies: Research Issues, Current Status, and Future Challenges

Systematic Review of Sleep Monitoring Systems for Babies: Research Issues, Current Status, and Future Challenges

CNN‐SCNet: A CNN net‐based deep learning framework for infant cry detection in household setting

Infant cry classification by using different deep neural network models and hand-crafted features

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