Inspired by deep learning applications in structural mechanics, we focus on how to train two predictors to model the relation between the vibrational response of a prescribed point of a wooden plate and its material properties. In particular, the eigenfrequencies of the plate are estimated via multilinear regression, whereas their amplitude is predicted by a feedforward neural network. We show that labeling the train set by mode numbers instead of by the order of appearance of the eigenfrequencies greatly improves the accuracy of the regression and that the coefficients of the multilinear regressor allow the definition of a linear relation between the first eigenfrequencies of the plate and its material properties.