A proof-of-concept study of estimating wind speed from acoustic frequency-domain signal using machine learning

Abstract

Wind speed measurement is one of the most fundamental tasks for multidiscipline applications and plays an important role in the design and maintenance of modern infrastructures. Wind speed is usually measured using conventional gauges which require additional connections to sensors or collection boxes, and their complex operating principles make these devices largely serve only professionals. This study proposed a novel framework associated with a machine learning architecture to estimate wind speed directly from acoustic signal collected using smartphones. The one-dimensional convolutional network is employed to characterize the underlying relationship between the frequency domain features of the acoustic signal and wind speed. An experimental dataset is collected in wind tunnel laboratory in which the wind speed is measured using cobra probe and the acoustic signal is recorded using smartphone. The influence of encountering direction angle on the 1D-CNN wind speed measurement model is also discussed, as well as the ability of the model to resist noise. The favorable robustness and generalization performance of the 1D-CNN model are verified from multiple perspectives, illustrating the feasibility and practical value of using smartphones to measure wind speed.