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Sound Intensity Level00:53

Sound Intensity Level

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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.
The human ear can perceive an extensive range of sound intensity, necessitating the use of the logarithmic scale to define a physical quantity—the intensity level. It is a ratio of two intensities and...
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Classification of Signals01:30

Classification of Signals

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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...
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Sound Intensity00:58

Sound Intensity

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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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相关实验视频

Updated: Jan 16, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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使用GRU-Mixer架构与Log-Mel谱图特征的语言疼痛水平分类.

Adi Alhudhaif1

  • 1Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|September 27, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了Gated Recurrent Unit (GRU) -Mixer,这是一个用于自动检测语音疼痛的深度学习模型. 它在分类疼痛水平方面达到很高的准确性,为非侵入性患者评估提供了一个有前途的工具.

关键词:
这是一个GRU-Mixer.罗格-梅尔光谱图 罗格-梅尔光谱图疼痛水平的确定 疼痛水平的确定语言信号 语音信号 语音信号

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科学领域:

  • 计算语言学计算语言学
  • 情感计算是一种情感计算.
  • 机器学习用于医疗保健

背景情况:

  • 语音自动疼痛检测提供非侵入性,实时评估,对于无法自我报告的患者至关重要.
  • 现有的方法需要进一步开发,以获得强大的临床应用.

研究的目的:

  • 介绍和评估Gated Recurrent Unit (GRU) -Mixer,这是一个基于语音的疼痛分类的新型深度学习模型.
  • 为未来有关情感计算和疼痛识别的研究建立一个基准.

主要方法:

  • 开发了一种轻量级的重复深度学习模型 (GRU-Mixer),从语音中处理Log-Mel光谱图.
  • 该模型使用堆叠的双向GRU和适应平均聚合来提取时间特征.
  • 使用类平衡损失的扬声器独立训练用于对二进制,分级强度和热状态疼痛分类任务的概括.

主要成果:

  • GRU-Mixer在二元疼痛检测 (疼痛与非疼痛) 中获得了83.86%的准确性.
  • 多类疼痛强度分类 (轻度,中度,严重) 达到75.36%的准确性.
  • 该模型在TAME疼痛数据集上表现出强的表现.

结论:

  • GRU-Mixer为基于语音的疼痛识别提供了一个有效的基准架构.
  • 这项研究是TAME Pain数据集上的第一个深度学习分类工作.
  • 这些发现支持AI在客观疼痛评估中通过声乐生物标志物的潜力.