基于多来源转移学习的滚轮链传输系统的声音质量研究
Jiabao Li1, Lichi An1, Yabing Cheng2
1School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China.
Scientific reports
|May 16, 2024
概括
一个新的多源传输学习卷积神经网络 (MSTL-CNN) 能够有效地预测滚筒链传输系统的声音质量. 这种方法克服了小型数据集和传统方法的局限性,在声音质量评估中提供了卓越的性能.
科学领域:
- 机械工程 机械工程
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 滚轮链传动系统会产生影响产品感知的噪音.
- 准确的声音质量评估至关重要,但由于有限的高质量噪音数据而受到挑战.
- 传统的方法难以应对声学数据的变化和复杂性.
研究的目的:
- 为滚轮链传动系统开发一个先进的声音质量评估模型.
- 为了应对数据集中不够高质量的声学样本的挑战.
- 提高声音质量预测的准确性和可靠性.
主要方法:
- 在不同的工作条件下收集并预处理运行噪声样本.
- 经验丰富的测试人员进行了主观评估,并使用Mel频率塞普斯特拉系数 (MFCC) 进行了客观评估.
- 提出了一种新的多源传输学习卷积神经网络 (MSTL-CNN),以利用多源任务的知识.
主要成果:
- 开发了一个综合数据集,将主观和客观的声音质量评估结合起来.
- MSTL-CNN有效地解决了声音质量预测中的小样本大小问题.
- 拟议的MSTL-CNN与传统的声音质量评估方法相比,表现明显更好.
结论:
- MSTL-CNN提供了一个强大的解决方案,用于在有限数据的滚筒链系统中进行声音质量预测.
- 多源转移学习提高了模型的概括性和准确性.
- 该研究推进了客观和主观的声音质量评估方法.
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