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Related Concept Videos

Auditory Pathway01:15

Auditory Pathway

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Auditory pathways constitute the complex neural circuits responsible for transmitting and interpreting auditory information from the peripheral auditory system to the brain. Sound waves are initially captured by the outer ear, funneled through the ear canal, and reach the tympanic membrane (eardrum). These vibrations are transmitted via the middle ear's ossicles to the inner ear's cochlea.
When viewed cross-sectionally, the cochlea reveals the scala vestibuli and scala tympani flanking...
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Perceiving Loudness, Pitch, and Location01:21

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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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The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the...
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Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
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Hearing01:31

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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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Correction: Kim et al. The Suppression of Ubiquitin C-Terminal Hydrolase L1 Promotes the Transdifferentiation of Auditory Supporting Cells into Hair Cells by Regulating the mTOR Pathway. <i>Cells</i> 2024, <i>13</i>, 737.

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Cascade recurring deep networks for audible range prediction.

Yonghyun Nam1, Oak-Sung Choo2, Yu-Ri Lee2

  • 1Department of Industrial Engineering, Ajou University, Suwon, Korea.

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|May 26, 2017
PubMed
Summary

A new machine learning algorithm predicts hearing aid effectiveness by analyzing hearing loss, device, and frequency characteristics. This approach significantly reduces prediction errors, improving patient satisfaction and quality of life.

Keywords:
Cascade structureDeep learningHearing AidsHearing improvementNeural networksRecurrent structure

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Area of Science:

  • Audiology
  • Machine Learning
  • Signal Processing

Background:

  • Hearing aids improve quality of life for individuals with hearing loss by amplifying specific frequencies.
  • Factors influencing hearing aid effectiveness include patient hearing loss, device specifications, and frequency characteristics.
  • Limited research exists on predicting hearing gain using all three factors simultaneously.

Purpose of the Study:

  • To develop a novel machine learning algorithm for predicting hearing improvement from hearing aid use.
  • To integrate patient hearing loss, hearing aid, and frequency characteristics for enhanced prediction accuracy.

Main Methods:

  • A cascade recurring deep network architecture was developed.
  • The network incorporates cascade structures for frequency band correlations and recurrent structures for variable reuse.
  • Training involves a two-phase approach: cascade and tuning phases.

Main Results:

  • The proposed algorithm was tested on medical records of 2,182 patients with hearing loss.
  • It achieved a 58% reduction in error rate compared to existing neural networks.
  • Demonstrated superior performance in predicting hearing gain.

Conclusions:

  • The developed algorithm is a novel solution for signal or sequential data analysis.
  • It can be applied to predict hearing aid outcomes effectively.
  • Clinically, it serves as a valuable tool to enhance patient satisfaction with hearing solutions.