Adamson ArrayIntelligence = Array Optimization

While mechanical adjustments in line arrays—such as array curvature, splay angles, and overall configuration—form the backbone of coverage shaping, they alone cannot achieve the nuanced uniformity demanded by modern productions. Recognizing this, Adamson Systems Engineering pioneered an advanced approach: a software-driven optimization algorithm that applies finely tuned FIR (Finite Impulse Response) filters to each cabinet in the array.

This approach, often termed “Line Source Optimization,” transcends the limitations of purely mechanical solutions. By integrating the mechanics of array design with powerful digital signal processing (DSP), the Adamson algorithm enables system engineers to achieve superior frequency response, coverage uniformity, and tonal consistency across the entire venue.

Core Concept: Traditional line array design relies largely on physical adjustments—i.e the geometric placement and orientation of individual cabinets—to shape coverage. While mechanical arrangement establishes a broad coverage pattern and ensures that each section of the audience receives a baseline SPL, certain trade-offs often remain. The relative on-axis and off-axis performance can vary, leading to tonal inconsistencies between the front, mid, and rear audience positions.

Adamson’s optimization algorithm takes this mechanical “starting point” and refines it electronically. It processes the entire array as a system of individually addressable elements, each capable of receiving custom FIR filters that alter magnitude and phase over defined frequency ranges. By doing so, it provides a dynamic, data-driven pathway to meet specific coverage and tonal objectives.

Virtual Microphones: Defining the Optimization Field

A key innovation within the Adamson approach is the concept of virtual microphone placement. These “virtual mics” are not physical measurement points, but rather computational markers distributed across the audience area (or a cross-sectional slice of it) in the software’s prediction environment.

Resolution Settings:

• Normal Resolution: Places a virtual microphone approximately every 1 meter.
• High Resolution: Places a virtual microphone approximately every 0.5 meter.

By capturing a denser or sparser set of spatial data points, the engineer can balance computational complexity and run-time with the fidelity of the optimization results. High resolution may yield more finely tuned corrections, particularly beneficial in challenging acoustic environments or complex audience geometries.

Before running the optimization, the user sets several key parameters:

1. Headroom Limit:
   Users specify the maximum allowable “headroom loss” due to filter application. This ensures that in pursuit of a flat, optimized response, the algorithm does not impose excessive gain reduction or limiting that could compromise overall system output. For example, setting this limit at ±3 dB restricts how aggressively the algorithm can boost or cut to maintain tonal balance.
    
2. Frequency Constraints:
   Engineers can define the optimization’s frequency boundaries through high-pass and low-pass filters. This allows concentrating processing power and corrections only where needed, or ignoring frequency bands best left unchanged (e.g., delicate HF content or extremely low-end information that is well-managed mechanically).
    
3. Target SPL Curve Editing:
   The target frequency response curve—what the optimization strives to achieve at each virtual microphone position—can be directly manipulated by the user. Through intuitive graphical tools, the user refines the desired tonal signature. This may involve “flattening” the response, maintaining a gentle slope for musical warmth, or customizing response for a particular style of content.

Once the parameters are set, the user initiates the optimization process. The algorithm analyses the current predicted SPL and frequency response at each virtual microphone, comparing it to the user’s target curve. It then iteratively applies FIR filters to each cabinet:

1. Iterative Adjustment of FIR Filters:
   Each cabinet receives a custom FIR filter that subtly alters its magnitude and phase response. Rather than guessing or broadly applying EQ, the algorithm “listens” to the predicted performance at each virtual mic point and incrementally refines the filter parameters to minimize the deviation between actual and target SPL/frequency response.
    
2. Frequency-Dependent Cabinet Contribution:
   At low frequencies, the entire array behaves as a cohesive unit, combining constructively (and sometimes destructively) to shape the low-end pattern. Here, the algorithm focuses on applying subtle phase corrections to improve low-frequency steering without sacrificing overall SPL. Because low frequencies are more omnidirectional and influenced by total array length and configuration, this part of the optimization often involves delicate timing adjustments.
   
   At higher frequencies, each cabinet’s contribution becomes more localized; listeners in different sections of the venue may experience drastically different cabinet-to-cabinet interactions. In these regions, the algorithm can apply finely tuned gain adjustments to individual cabinets. By doing so, it compensates for the natural roll-off that might occur off-axis or from more distant cabinets, smoothing out the tonal balance front to back.
    
3. Non-Destructive Constraints:
   If at any point the calculated FIR filters exceed the defined headroom limit, the algorithm scales back all filters proportionally. This holistic scaling ensures that no single cabinet’s correction undermines the overall tonal balance. Rather than clipping or causing artifacts, the algorithm maintains a stable equilibrium, ensuring cohesive and non-destructive optimization.

After the algorithm completes its calculations, the user can inspect the results. Clicking on a virtual microphone position displays the predicted SPL and frequency response at that exact point, allowing the engineer to verify improvements and confirm that the optimization goals have been met.

Because the optimized filters preserve time alignment, particularly with subwoofers, engineers can switch the optimization on and off to compare system performance. This makes A/B comparisons straightforward and ensures that the fundamental character of the system—especially the crucial timing relationship between subwoofers and full-range elements—remains stable.

Mechanical First, Digital Second:
Adamson’s optimization algorithm is not a magic bullet for poor mechanical design. In fact, it is most effective when applied to arrays that are already well-conceived mechanically. By starting with a well-curved array that properly aims energy towards the intended audience areas, the optimization algorithm refines and fine-tunes the results, rather than compensating for a fundamentally flawed setup.

Preserving the Impulse Response:
The optimization is designed to enhance direct sound without introducing damaging phase anomalies or undue coloration. The refined filters are subtle enough to improve evenness and tonality without destroying transient clarity. This means that the musicality and intelligibility of the system is preserved or even improved.

Reduced Setup Time and Better Consistency:
Because the optimization automates and systematizes what used to be a tedious, iterative manual EQ and tuning process, it helps engineers achieve consistent, high-quality results faster. Live sound professionals gain the confidence that their arrays are performing at peak potential and that the sound quality delivered to every seat is as uniform as possible.

Adamson’s Array Optimization algorithm represents a leap forward in large-scale sound reinforcement design and deployment. By blending traditional mechanical engineering with advanced FIR-based DSP, the algorithm ensures that modern line arrays achieve unprecedented uniformity in coverage and tonality. Through careful virtual microphone placement, user-defined frequency and headroom constraints, and intelligent iterative filtering, the system can seamlessly bridge the gap between predicted performance and the real-world listening experience.

As live audio systems become ever more complex and expectations continue to rise, such digital optimization strategies will only grow in importance. Adamson’s approach sets a standard for how cutting-edge DSP can be integrated into the design and deployment process, offering engineers both efficiency and a higher level of sonic excellence. It’s a glimpse into the future of pro-audio system optimization—one that continues to push the boundaries of what can be achieved with advanced technology and thoughtful design.

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By Travis Taylor 
National Technical Sales & Systems Design