Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties

Abstract: Accurate prediction of energy decay curves (EDCs) enables robust analysis of room acoustics and reliable estimation of key parameters. We present a deep learning framework that predicts EDCs directly from room geometry and surface absorption. A dataset of 6000 shoebox rooms with realistic dimensions, source-receiver placements, and frequency-dependent wall absorptions was synthesized. For each configuration we simulate room impulse responses (RIRs) using Pyroomacoustics and compute target EDCs. Normalized room features are provided to a long short-term memory (LSTM) network that maps configuration to EDC. Performance is evaluated with mean absolute error (MAE) and root mean square error (RMSE) over time. We further derive early decay time (EDT), reverberation time (T20), and clarity index (C50) from predicted and target EDCs; close agreement is observed (e.g., EDT MAE 0.017 s, T20 MAE 0.021 s). The approach generalizes across diverse rooms and supports efficient room-acoustics modeling for early-stage design and real-time applications.