Acoustic mode decomposition is commonly used to separate pressure wave components in flow ducts and established methods relay on mathematical descriptions of the wave motion. In this work, a new approach to mode decomposition is presented that uses artificial intelligence to separate the acoustic mode content. A neural network is trained with data gained from a small set of numeric solutions of the linearized Navier-Stokes equation in the frequency domain in a straight duct. The network is tested on relevant experimental data. A good agreement with the analytical method is demonstrated in two applications with different flow conditions. We conclude that that using AI for mode decomposition is a promising alternative to the standard method as it is applicable to a much broader set of aero-acoustic problems.