AI-Driven Real-Time Acoustic Modelling for Better Audio Perception in Dynamic Environments

摘要

This paper presents an AI-driven framework for real-time reverberation control in dynamic environments. The system integrates parametric modeling in Grasshopper, Pachyderm acoustic simulation, and machine learning to create a closed-loop controller. A CNN estimates reverberation time from audio signals, while a reinforcement learning agent dynamically adjusts panel absorption coefficients to maintain optimal acoustics. Evaluation showed the system should be able to maintain T60 within 0.15 s of the target under varying occupancy and source positions, outperforming static treatments and enabling self-regulating acoustic environments for improved auditory experiences.