To date, vibrational simulation results constitute more of an experimental support than a predictive tool, as the simulated vibrational modes are discrete due to quantization. This is different from what is obtained experimentally. Here, we propose a way to combine outputs such as the phonon density of states surrogate and peak intensities obtained from ab initio simulations to allow comparison with experimental data by using machine learning. This work is paving the way for using simulated vibrational spectra as a tool to identify materials with defined stoichiometry, enabling the separation of genuine vibrational features of pure phases from morphological and defect-induced signals.