This article focuses on the prediction of the vibration frequency response of handheld probes. A novel approach that involves machine learning and readily available data from probes was explored. Vibration probes are efficient and affordable devices that provide information about testing airborne sound insulation in building acoustics. However, fixing a probe to a vibrating surface downshifts sensor resonancesi and underestimates levels. Therefore, the calibration response of the sensor included in a probe differs from the frequency response of that same probe. Simulation techniques of complex mechanical systems may describe this issue, but they include hardly obtainable parameters, ultimately restricting the model. Thus, this study discusses an alternative method, which comprises different parts. Firstly, the vibration frequency responses of 85 probes were measured and labelled according to six features. Then, Linear Regression, Decision Tree Regression and Artificial Neural Networks algorithms were analysed. It was revealed that decision tree regression is the more appropriate technique for this data. The best decision tree models, in terms of scores and model structure, were fine-tuned. Eventually, the final suggested model employs only four out of the six original features. A trade-off solution that involved a simple structure, an interpretable model and accurate predictions was accomplished. It showed a maximum average deviation from test measurements ranging from 0.6 dB in low- frequency to 3 dB in high-frequency while remaining at a low computational load. This research developed an original and reliable prediction tool that provides the vibration frequency response of handheld probes.

中文翻译：

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决策树回归在手持式探头振动频率响应预测中的应用

本文重点介绍手持式探头振动频率响应的预测。探索了一种涉及机器学习和来自探针的现成数据的新方法。振动探头是一种高效且价格合理的设备，可提供有关测试建筑声学中空气声隔离的信息。然而，将探头固定到振动表面会降低传感器共振并低估水平。因此，包含在探头中的传感器的校准响应与同一探头的频率响应不同。复杂机械系统的仿真技术可以描述这个问题，但它们包含难以获得的参数，最终限制了模型。因此，本研究讨论了一种替代方法，该方法包括不同的部分。首先，根据六个特征测量并标记了85个探头的振动频率响应。然后，分析了线性回归、决策树回归和人工神经网络算法。结果表明，对于这些数据，决策树回归是更合适的技术。在分数和模型结构方面，对最佳决策树模型进行了微调。最终，最终建议的模型仅使用了六个原始特征中的四个。完成了一个涉及简单结构、可解释模型和准确预测的权衡解决方案。它显示与测试测量的最大平均偏差范围从低频的 0.6 dB 到高频的 3 dB，同时保持低计算负载。