使用优化卷积神经网络和基于人工智能的可解释性分析来检测语音病理
1Department of Electronics and Communication Engineering, BMS College of Engineering, Bengluru, Karnataka, India.
Computer methods in biomechanics and biomedical engineering
|October 18, 2023
概括
这项研究引入了一个优化的卷积神经网络 (CNN) 用于检测健康和病态的声音. 人工蜂群 (ABC) 算法提高了CNN的准确性,改善了语音障碍的检测.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 区分健康和病态的声音对于诊断语音障碍至关重要.
- 传统方法可能缺乏对微妙的声音变化所需的精度.
- 计算机辅助诊断为客观的语音评估提供了一个有希望的途径.
研究的目的:
- 为健康和病态语音检测提出一种非侵入性的计算机辅助评估方法.
- 优化卷积神经网络 (CNN) 使用人工蜂群 (ABC) 算法进行增强的语音分析.
- 为了验证拟议的方法在不同语音数据集上的性能.
主要方法:
- 语音样本被转化为时间频率表示的Mel谱图.
- 一个CNN模特被训练使用这些光谱图来进行语音分类.
- 通过人工蜂群 (ABC) 算法优化了CNN的参数.
- 为了解释性,使用了梯度加权类激活映射 (Grad-CAM).
主要成果:
- 与传统的CNN相比,ABC优化的CNN模型的准确性提高了1.02%.
- 该方法显示了数据独立的歧视性表示能力.
- 在SVD,AVPD和VOICED数据集上进行了评估,证实了强大的性能.
结论:
- 建议的非侵入性,优化的CNN方法有效地检测病态声音.
- ABC优化显著提高了CNN在语音分析中的性能.
- 使用Grad-CAM的可解释AI (XAI) 在诊断决策中提供了透明度.
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