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相关概念视频

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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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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在不同振动源的儿科失声症中基于细分的信号类型和预测建模.

Yeonggwang Park1, Supraja Anand2, Susan Baker Brehm3,4

  • 1School of Communication Sciences and Disorders, University of Central Florida, Orlando.

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概括

这项研究改进了儿童发声障碍的语音信号输入,提高了声学测量可靠性. 一个自动化工具在识别不同类型的语音信号方面取得了很高的准确性,提高了临床使用.

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科学领域:

  • 语音语言病理学 语言病理学
  • 声学分析 声学分析
  • 声音障碍 声音障碍

背景情况:

  • 儿童严重的听力障碍会给声学分析带来挑战,原因是信号的非周期性.
  • 目前的语音信号输入方法是主观的,可能无法在样本中捕获多个信号类型.

研究的目的:

  • 完善手动信号打字工具,用于对儿科语音信号进行分段级标记.
  • 开发一个客观的,用于儿童自动语音信号打字的预测模型.

主要方法:

  • 专家语音语言病理学家手动标记了来自儿童的声音样本,这些孩子具有状和状上状振动源.
  • 一个预测模型被训练使用声学措施,如音调强度,EnvSD8,度和CPPS.
  • 模型性能使用测试集和交叉验证进行了评估.

主要成果:

  • 手动打字在总体11%的样本和20%的样本中识别了多种信号类型,其中有SGVS的样本.
  • 预测模型在分类信号类型方面实现了81%-96%的准确性.
  • 关键的声学指标 (EnvSD8,CPPS,清晰度) 是有效的预测指标.

结论:

  • 精致的手动工具提高了信号输入精度,有可能提高声学测量可靠性,并使新的结果指标成为可能.
  • 使用客观测量的自动信号输入为儿科语音分析提供了显著的临床实用性.