  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 resonances 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.
