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相关概念视频

Hearing01:31

Hearing

52.0K
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.
52.0K
Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

205
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.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
205
Perception of Sound Waves01:01

Perception of Sound Waves

4.4K
The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
4.4K
The Cochlea01:13

The Cochlea

44.7K
The cochlea is a coiled structure in the inner ear that contains hair cells—the sensory receptors of the auditory system. Sound waves are transmitted to the cochlea by small bones attached to the eardrum called the ossicles, which vibrate the oval window that leads to the inner ear. This causes fluid in the chambers of the cochlea to move, vibrating the basilar membrane.
44.7K
Classification of Signals01:30

Classification of Signals

437
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
437

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相关实验视频

Updated: Jun 23, 2025

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss
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Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Published on: January 25, 2016

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自动听力损失类型分类基于纯音调听力测量数据.

Michał Kassjański1, Marcin Kulawiak2, Tomasz Przewoźny3

  • 1Department of Geoinformatics, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, G. Narutowicza 11/12, 80-233, Gdańsk, Poland. michal.kassjanski@pg.edu.pl.

Scientific reports
|June 20, 2024
PubMed
概括

一个新的AI模型从听力测量结果中准确地分类听力损失类型,加速诊断. 这种工具有助于全科医生和专家,提高了听力护理的准确性,减少了听力护理的延迟.

关键词:
人工智能决策支持系统音频节目 音频节目 音频节目这是一个双LSTM.分类 分类 分类 分类.听力损失 听力损失音色声度测试的音色声度测试方法

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Assessment of Audio-Tactile Sensory Substitution Training in Participants with Profound Deafness Using the Event-Related Potential Technique

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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages
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Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

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相关实验视频

Last Updated: Jun 23, 2025

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科学领域:

  • 听力学 听力学是指听力学.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 音调听力测量是诊断听力损失的标准,但解释需要专家,导致诊断延迟.
  • 听力学家的有限可用性会影响及时诊断和治疗听力障碍.

研究的目的:

  • 开发和评估一个神经网络用于自动分类的音调听力测量数据.
  • 将听力损失分为四类:正常,导电,混合和感应神经.

主要方法:

  • 使用双向长短期记忆 (BiLSTM) 神经网络架构.
  • 训练模型的15046个听力测量结果由专业的听力学家分析和分类.

主要成果:

  • 在一个独立的数据集上实现了99.33%的分类准确性.
  • 该模型有效地区分正常听力和不同类型的听力损失.

结论:

  • 人工智能模型提供了一种可靠的方法来分类音调听力测量结果,可能减少诊断负担.
  • 允许全科医生对转诊结果进行分类,并支持听力学家/耳鼻科医生作为决策支持工具.