System Design Exchange:
The Standard for Noise Predictions with Sound Systems

As outdoor events in urban areas continue to increase, effective and reliable noise management has become more important than ever. Yet predicting noise from large open-air events has long been a complex and inconsistent process.

Fragmented Workflows and Data Silos

The core issue was not a lack of expertise, but the workflow itself. When multiple sound system manufacturers were involved, different proprietary data formats and calculation approaches could come into play. While each system worked well within its own design environment, there was no standardized way to exchange a complete stage setup for environmental noise prediction. Engineers often had to work with multiple software tools, export and convert data, manually position sources in environmental noise software, and approximate directivities or spectra.

Coherent Sources require Phase

Beyond the data exchange, what was also missing was a consistent environmental acoustic calculation methodology that properly considered phase information and coherence effects. Traditional environmental noise models typically treat sources as incoherent. However, sound systems, especially line arrays and multiple arrays, are coherent sources where phase interactions significantly influence the resulting sound field. Without a physically consistent approach that integrates phase into the propagation calculation, results could vary considerably.

Consequences in Practice

The workflow was therefore time-consuming and often frustrating. Every conversion step introduced potential errors. Phase information was frequently simplified or lost. Interactions between arrays were not fully represented. Calibration at front of house was especially problematic because far-field directivities were applied to near-field situations.

An additional challenge arose during the planning phase itself. Event planning is rarely static: layouts change, stage positions are adjusted, system configurations are optimized, regulatory constraints require modifications. Each of these changes often meant repeating the entire workflow: exporting data again, converting formats, redefining sources, recalibrating levels, and recalculating scenarios. What should have been an iterative optimization process became a repetitive and error-prone sequence of manual steps.

Uncertainty for All Stakeholders

As a result, predictions could differ significantly depending on the chosen method, the assumed reference distance for directivity data, or the way sources were grouped. In some cases, differences of more than 10 dB occurred; not because physics changed, but because the methodology did.

For engineers, this created uncertainty. For authorities, it reduced comparability. And for event organizers, it made regulatory compliance more difficult to demonstrate.

This is where SDE comes in.