Soundscape: A Machine Learning Approach to Predict Acoustic Performance in Diverse Spatial Settings

During the initial phases of design, engineers and architects lack quick and accurate ways to evaluate acoustic performance, instead depending on laborious full-scale simulations or simplified, often imprecise formulas. The goal of this study is to create a machine-learning framework that can forecast important acoustic performance metrics from various spatial configurations and sound absorption patterns. A dataset of room geometries (dimensions, shape parameters, geometric descriptors) and absorption coefficients (walls, ceiling, floor) has been generated via a digital design pipeline, and acoustic simulations have been executed to obtain target metrics (e.g., sound pressure level, reverberation time, and clarity indices). This dataset was then used to train a neural network that could be integrated into early-stage design workflows, enabling geometry tweaks and material decisions to be evaluated acoustically in real time. This presentation reports mid-term findings, including dataset composition and training strategy, and discusses initial prediction accuracy.