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In this paper, we propose a method to quantify the likelihood that perforated exterior façade panels will produce wind-induced noise. This noise is generated by pressure fluctuations when vortices shed off the panels. The frequency and audibility are influenced by panel geometry, wind velocity, turbulence, and wind angle of incidence. We performed parametric computational fluid dynamic (CFD) analyses of perforated panels to calculate sound pressure levels and trained a machine learning algorithm on that data. We applied the learned relationship to a building scale CFD analysis accounting for annual variation of wind direction and strength to predict the likelihood of noise at any location on the building for a typical weather year.