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相关概念视频

Language Development01:22

Language Development

365
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
365

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基于机器学习的婴儿哭泣解释

Mohammed Hammoud1, Melaku N Getahun1, Anna Baldycheva2

  • 1Digital Engineering CREI, Skolkovo Institute of Science and Technology, Moscow, Russia.

Frontiers in artificial intelligence
|February 23, 2024
PubMed
概括
此摘要是机器生成的。

对护理人员来说,解释婴儿的哭泣是个挑战. 这项研究开发了一种先进的机器学习模型,使用Mel-frequency cepstral系数 (MFCC) 和随机森林分类器来准确解码婴儿救援信号.

关键词:
梅尔频率的塞普斯特拉尔系数音频处理 音频处理机器学习是机器学习.频谱图是指光谱图中的光谱.时间序列分类时间序列分类.时间序列的想象力可以想象时间序列.

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科学领域:

  • 婴儿通信和信号处理.
  • 机器学习在医疗保健中的应用.

背景情况:

  • 婴儿的哭泣是主要的沟通方式,护理人员往往难以解释.
  • 误解婴儿的哭声可能会导致婴儿福祉的重大问题.
  • 现有的哭声分析方法需要改进音频特征表示和分类.

研究的目的:

  • 开发一个准确的婴儿哭泣分析系统.
  • 为了评估各种音频特征表示和机器学习分类器的声音解释.
  • 根据发声识别婴儿状态的最佳特征和模型,以区分婴儿状态.

主要方法:

  • 使用的时间域 (零交叉率,根平均平方),频域 (梅尔谱图) 和时间频域 (梅尔频率塞普斯特拉系数 - MFCC) 音频功能.
  • 应用时间序列成像算法,将MFCC特征转化为视觉表示.
  • 训练并评估了多个机器学习分类器,包括决策树,随机森林,K-最近邻居和包装.

主要成果:

  • 麦尔频率塞普斯特拉系数 (MFCC),零交叉率 (ZCR) 和根平均平方 (RMS) 特性表现出高性能.
  • 基于MFCC的随机森林 (RF) 分类器实现了96.39%的准确率.
  • 这一性能超过了基于刻度图的最先进的ShuffleNet分类器 (95.17%准确率).

结论:

  • 拟议的基于MFCC的随机森林方法为婴儿哭声分析提供了一种高度有效的方法.
  • 这项研究强调了先进的音频特征提取和机器学习对于理解婴儿沟通的重要性.
  • 这些发现提供了一个有希望的工具,以帮助护理人员更准确地解释婴儿的需求.