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基于混合音频特征和ResLSTM的婴儿哭泣的分类
Yongbo Qiu1, Xin Yang1, Siqi Yang1
1School of Electronic and Electrical Engineering, Chongqing University of Science and Technology, Chongqing, China.
使用混合特征集 (Mel 频率 Cepstral 系数,Mel 谱图和Tonnetz) 和ResLSTM模型分析婴儿的哭声,可以显著提高婴儿沟通的分类准确性.
科学领域:
- 婴儿沟通和发育心理学
- 机器学习在医疗保健中的应用.
- 信号处理用于生物信号分析.
背景情况:
- 婴儿的哭泣是关键的沟通方式,信号需要,如饥饿或不适.
- 准确的哭声分析可以支持新父母在婴儿护理.
- 以前的研究通常依赖于单一的语音特征,忽视其他特征.
研究的目的:
- 为了评估婴儿哭声分类的新型混合特征集 (MMT).
- 引入和测试一个ResLSTM深度学习模型用于哭声分析.
- 将MMT和ResLSTM的性能与传统方法进行比较.
主要方法:
- 设计了一个混合功能集,结合了Mel频率塞普斯特拉系数 (MFCC),Mel光谱和Tonnetz (MMT).
- 开发了一个深度学习模型,ResLSTM,集成剩余连接和长短期记忆网络.
- 在三个不同的婴儿哭声数据集上评估了MMT和ResLSTM.
主要成果:
- 混合MMT功能集的性能优于单一的MFCC功能.
- 与MMT相结合的ResLSTM模型实现了更高的分类准确性.
- 在数据集中,准确率达到94.15%,92.92%和95.98%.
结论:
- 混合功能集与单一功能相比,提供了增强的婴儿哭声分析.
- ResLSTM模型在分类婴儿哭声方面表现出高效率.
- 这种方法有可能改善婴儿护理支持系统.
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