Soundscape: A Machine Learning Approach to Predict Acoustic Performance in Diverse Spatial Settings
Year: 2025
Type: article
Abstract: 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.
Author: Hendrik Wiese
Institution: Leibniz University Hannover
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7101675456
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