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In recent years, the adoption of surrogate modeling in vibroacoustics applications is being explored. Artificial Neural Networks (ANNs) are proving their effectiveness in predicting structural vibration, and more recently their capabilities in sound radiation prediction is being investigating as well, paving the way for new research in the field. This paper explores the use of a Deep Neural Network (DNN) approach for the prediction of the sound field of plates withone orthogonal stiffener. In particular, the surrogate model can estimate the Sound Pressure Level (SPL) over a hemispherical surface enclosing a baffled plateof fixed dimensions, where the position of the stiffener and the forcing point is changed. The dataset for the network training is generated by numericalsimulations combining FEM, Rayleigh integral and modal superposition. The results prove this approach to be promising and effective for tackling complex vibroacoustic problems.