From Numbers to Perception, Energy Decay Curves Prediction

TL;DR

This paper presents a neural network framework that predicts energy decay curves from room geometry and materials, improving acoustic modeling for virtual environments.

Contribution

A novel neural network approach with a custom loss function that accurately predicts perceptually relevant energy decay curves from room parameters.

Findings

Successfully approximates ground-truth acoustics with minimal error in T30 and clarity.

Offers a computationally efficient alternative to traditional acoustic simulation methods.

Abstract

Predicting Room Impulse Responses (RIRs) remains a challenge due to the high dimensionality of audio signals and the need for perceptual accuracy. This paper introduces a neural network framework that predicts multi-band Energy Decay Curves (EDCs) directly from room geometry and material properties. Unlike standard models, our framework employs a custom composite loss function that optimizes for both energy levels and decay slopes in the log-domain. This ensures the predicted curves adhere to physical decay principles while maintaining high sensitivity to reverberation time and early reflections. Results demonstrate that the model successfully approximates ground-truth acoustics with minimal error in T30 and clarity indices. The approach offers a computationally efficient alternative to traditional simulations, facilitating realistic audio rendering for interactive virtual environments.