Room Impulse Response Prediction with Neural Networks: From Energy Decay Curves to Perceptual Validation

Prediction of room impulse responses (RIRs) is essential for room acoustics, spatial audio, and immersive applications, yet conventional simulations and measurements remain computationally expensive and time-consuming. This work proposes a neural network framework that predicts energy decay curves (EDCs) from room dimensions, material absorption coefficients, and source-receiver positions, and reconstructs corresponding RIRs via reverse-differentiation. A large training dataset was generated using room acoustic simulations with realistic geometries, frequency-dependent absorption, and diverse source-receiver configurations. Objective evaluation employed root mean squared error (RMSE) and a custom loss for EDCs, as well as correlation, mean squared error (MSE), spectral similarity for reconstructed RIRs. Perceptual validation through a MUSHRA listening test confirmed no significant perceptual differences between predicted and reference RIRs. The results demonstrate that the proposed framework provides accurate and perceptually reliable RIR predictions, offering a scalable solution for practical acoustic modeling and audio rendering applications.