From Numbers to Perception, Energy Decay Curves Prediction

Imran Muhammad; Gerald Schuller

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arXiv · 2605.20968

From Numbers to Perception, Energy Decay Curves Prediction

Imran Muhammad·Gerald Schuller

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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.

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BibTeX RIS

Imran Muhammad, Gerald Schuller. 2026-05-20. From Numbers to Perception, Energy Decay Curves Prediction. https://arxiv.org/abs/2605.20968

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