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Related Concept Videos

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Humans perceive sound by hearing. The human ear helps sound waves reach the brain, which then interprets the waves and creates the perception of hearing. The loudness of the environment in which a person is located determines whether they can distinguish between different sound sources.
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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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The loudness of a sound source is related to how energetically the source is vibrating, consequently making the molecules of the propagation medium vibrate. To measure the loudness of a source, the physical quantity of interest is the intensity. This is defined as the energy emitted per unit of time per unit of area perpendicular to the sound wave's propagation direction. Since the total energy is greater if the source vibrates for a longer duration and over a larger area, dividing the...
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Related Experiment Video

Updated: Jun 26, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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A novel approach for estimating initial sound level for speech reception threshold test.

Heonzoo Lee1, Rayoung Park1,2, Sejin Kim3

  • 1Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju, Korea.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|May 17, 2024
PubMed
Summary

A new machine learning method accurately estimates the initial sound level for speech reception threshold (SRT) tests. This approach significantly reduces test repetitions and overall hearing assessment time, improving efficiency for patients and audiologists.

Keywords:
Hearing testconvolutional neural networkpure tone thresholdspeech audiometryspeech reception threshold

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Area of Science:

  • Audiology
  • Machine Learning in Healthcare
  • Signal Processing

Background:

  • The speech reception threshold (SRT) is the minimum hearing level to understand 50% of speech.
  • Current SRT measurement involves repetitive stimulus adjustments, leading to patient and audiologist fatigue.
  • This fatigue can compromise the reliability of hearing test results.

Purpose of the Study:

  • To develop a more optimal initial sound level estimation for SRT testing.
  • To reduce the number of repetitive adjustments required during SRT measurement.
  • To enhance the efficiency and reliability of hearing assessments.

Main Methods:

  • Implementation of a novel machine learning approach using a 1-dimensional convolutional neural network (1D CNN).
  • The 1D CNN was trained to predict a superior initial sound level compared to conventional methods.
  • The proposed method aims to estimate an initial SRT level closer to the final determined value.

Main Results:

  • The machine learning approach achieved a 37.92% reduction in the difference between the initial stimulus level and the final SRT.
  • This indicates a more accurate prediction of the starting point for the SRT test.
  • The results demonstrate the potential for significant improvement over traditional methods.

Conclusions:

  • The proposed 1D CNN method effectively reduces repetitions needed to determine the final SRT.
  • This reduction in test repetitions leads to a shorter overall hearing test duration.
  • The findings suggest a more efficient and potentially more reliable method for audiological assessments.