Influence of Geometrical Acoustic Simulator Complexity on a Trained Multisource Localizer
Learning-based acoustic sound source localization and detection (SSLD) requires large labeled datasets covering diverse acoustic conditions. Since obtaining measured data is costly, training commonly uses simulated data, while practical devices must operate under real-world conditions. However, higher simulation complexity increases data-generation cost, and the complexity required for reliable generalization remains unclear. In practice, SSLD methods often rely on efficient geometrical acoustic simulation, typically the image-source method. This work investigates how geometrical acoustic simulation complexity affects the real-world performance of a common multi-source SSLD model. We train the model with simulators ranging from anechoic conditions to high-order image-source simulations, optionally including diffuse reverberation, array simulation, and randomized image-source positions, and evaluate them on three measurement-based test datasets. Results show that anechoic simulation is insufficient, while medium-complexity image-source simulations already provide strong real-world performance. Further increases in complexity yield only marginal gains, with the best performance obtained using the highest tested image-source order, array simulation, and randomized image-source positions. For this configuration, measured-domain performance approaches within-domain simulated performance, suggesting limited benefit from further increasing simulation complexity. These findings show how the complexity-performance trade-off can be exploited in future data-driven SSLD, and highlight image-source randomization as an efficient way to improve generalization.