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Physics-informed neural networks (PINNs) have shown promise in solving partial differential equations (PDEs) but struggle with multi-scale features and high-frequency components due to spectral bias, leading to challenges like solution thrashing and poor convergence for wave equations. Recent advancements, such as finite basis PINNs and fourier feature PINNs have shown to address these issues for numerous PDEs but remain underexplored for wave equations. In this study, we compare vanilla PINNs, FBPINNs, and fourier feature PINNs in solving inhomogeneous acoustic wave equation. Using complex velocity models, we evaluate their performance and computational trade-offs, providing valuable insights into the practical applicability of these modifications for wave propagation simulation.