Speaker
Description
The advent of robotic-aided acoustic measurements has enabled the precise acquisition of Room Impulse Responses (RIRs) across dense sampling grids, which is essential, e.g., for documenting the unique acoustics of cultural heritage sites and music venues. Despite these technological advances, capturing a high volume of RIRs in expansive spaces remains a time-intensive process, and determining the most salient measurement points would be ideal to optimize this process. This study investigates the use of basic room simulations to predict the necessary spatial density of RIR measurements. Our approach is perceptually driven, incorporating the Just Noticeable Differences (JNDs) of objective acoustic parameters as defined by ISO 3382-1, such as Early Decay Time (EDT) or Clarity (C80).Our predictions are grounded in physical measurements from a high-density RIR dataset and benchmarked against a basic geometric-acoustic simulation of the same environment. Results from the case study show that such simulations can effectively estimate the required measurement resolution. Moreover, the optimal resolution depends on the chosen objective metrics and their associated JNDs.Simulation-based prediction thus provides a practical framework for optimizing measurement strategies and reducing the time required for dense RIR acquisition. In addition, these predictions enable the detection of anomalies and potential measurement errors during the acquisition process.