Scholarlink

<p>Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more comprehensive understanding of crying. To address this, we employed a convolutional neural network to assess whether whole Mel-spectrograms of infant crying capture gestational age (GA) variations (79 preterm infants; 52 term neonates). Our convolutional neural network models showed high accuracy in classifying gestational groups (92.4%) and in estimating the relative and continuous differences in GA (<i>r</i> = 0.73; <i>p</i> &lt; 0.0001), outperforming previous studies. Grad-CAM and spectrogram manipulations further revealed that GA variations in infant crying were prominently reflected in temporal structures, particularly at the onset and offset regions of vocalizations. These findings suggest that decoding spectrotemporal features in infant crying through deep learning may offer valuable insights into atypical neurodevelopment in preterm infants, with potential to enhance early detection and intervention strategies in clinical practice.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Listening deeper: neural networks unravel acoustic features in preterm infant crying

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

查看全文
  • Yuta Shinya,
  • Taiji Ueno,
  • Masahiko Kawai,
  • Fusako Niwa,
  • Seiichi Tomotaki,
  • Masako Myowa

摘要

Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more comprehensive understanding of crying. To address this, we employed a convolutional neural network to assess whether whole Mel-spectrograms of infant crying capture gestational age (GA) variations (79 preterm infants; 52 term neonates). Our convolutional neural network models showed high accuracy in classifying gestational groups (92.4%) and in estimating the relative and continuous differences in GA (r = 0.73; p < 0.0001), outperforming previous studies. Grad-CAM and spectrogram manipulations further revealed that GA variations in infant crying were prominently reflected in temporal structures, particularly at the onset and offset regions of vocalizations. These findings suggest that decoding spectrotemporal features in infant crying through deep learning may offer valuable insights into atypical neurodevelopment in preterm infants, with potential to enhance early detection and intervention strategies in clinical practice.