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Related Concept Videos

Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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Related Experiment Video

Updated: Jul 6, 2025

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses
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Auditory chaos classification in real-world environments.

Priyanka Khante1, Edison Thomaz1, Kaya de Barbaro2

  • 1Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States.

Frontiers in Digital Health
|January 5, 2024
PubMed
Summary
This summary is machine-generated.

Researchers developed an objective audio classifier to measure household chaos, a risk factor for child development. This tool uses child-worn audio recordings to provide high-resolution chaos predictions, aiding future research and interventions.

Keywords:
auditory classificationdeep learningdevelopmental psychologyhousehold chaosreal-world dataset

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Area of Science:

  • Child development
  • Computational linguistics
  • Environmental psychology

Background:

  • Household chaos is a known risk factor impacting child development.
  • Current measurement methods (parent surveys) limit research into bidirectional relationships between chaos and child behavior.
  • Objective, high-resolution measurement is needed to understand these dynamics.

Purpose of the Study:

  • To develop and validate an objective classifier for measuring household chaos using audio recordings.
  • To create a novel, large-scale annotated dataset of household auditory chaos.
  • To enable more precise research into the effects of household chaos on child development.

Main Methods:

  • Trained a classifier using over 411 hours of daylong audio recordings from infants' homes.
  • Annotated audio data to label ground-truth auditory chaos levels.
  • Utilized a sound event classifier to improve annotation efficiency.

Main Results:

  • The best-performing model achieved a macro F1 score of 0.701 for classifying four levels of household chaos.
  • Achieved a weighted F1 score of 0.679, demonstrating robust performance.
  • The developed classifier provides objective, high-resolution predictions of household chaos.

Conclusions:

  • Objective audio-based chaos prediction facilitates basic science research on child development.
  • High-resolution data can be used for targeted interventions to mitigate negative impacts of chaos.
  • The publicly released dataset supports further research and model development in this area.