抗噪声生物声学特征提取方法及其对音频分类性能的影响:系统性审查
Geofrey Owino1, Bernard Shibwabo1
1School of Computing and Engineering Sciences, Strathmore University, P.O. Box 75584, Nairobi, 00200, Kenya, 254 721913968.
JMIR biomedical engineering
|December 16, 2025
概括
耐噪技术改善了生态和新生儿健康监测的生物声学分类. 然而,这些抗噪方法在现实世界中的部署仍然有限,这凸显了需要进一步的研究和标准化.
科学领域:
- 生物声学是一种生物声学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 生物声学分类对于生态监测和新生儿健康监测至关重要.
- 环境噪音和信号变化挑战模型的可靠性.
- 强大的特征提取和消噪对于准确的声学事件解释至关重要.
研究的目的:
- 系统地审查噪声抗性特征提取和生物声学分类的无声化方面的进展.
- 探索方法趋势,模型类型和跨领域的可转移性.
- 评估在临床和生态环境中实际部署的证据.
主要方法:
- 在2024年12月之前对8个电子数据库进行系统审查.
- 包括使用机器/深度学习考虑噪音的基于音频的分类研究.
- 数据提取和质量评估由两名独立审查员使用TRIPOD检查清单.
主要成果:
- 132项研究符合资格标准,其中深度学习和混合模型占主导地位.
- 特征提取在96.2%的研究中存在,通常使用Mel频率CEPSTRAL系数和光谱图.
- 47%的研究采用了耐噪方法,但只有14.4%的研究证明了现实世界的部署.
结论:
- 耐噪技术提高了分类性能,但面临着有限的现实应用.
- 挑战包括数据集异质性,不一致的报告和依赖合成噪音.
- 未来的研究应该集中在协调的基准,跨领域的概括和部署策略上.
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