{"ID":2863641,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24834","arxiv_id":"2509.24834","title":"Room Impulse Response Prediction with Neural Networks: From Energy Decay Curves to Perceptual Validation","abstract":"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.","short_abstract":"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 dime...","url_abs":"https://arxiv.org/abs/2509.24834","url_pdf":"https://arxiv.org/pdf/2509.24834v1","authors":"[\"Imran Muhammad\",\"Gerald Schuller\"]","published":"2025-09-29T14:18:20Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[]","has_code":false}