TLDR;
This video explains why different measurement techniques in car audio tuning produce varying results and how these differences impact consistency, repeatability, and decision-making. It emphasizes understanding the assumptions and tradeoffs of each method to make informed tuning decisions, rather than blindly chasing specific traces. The video advocates for controlling variables, simplifying the measurement process, and using repeatable methods for consistent results.
- Different measurement techniques yield different results due to physics, not measurement errors.
- Understanding what measurements are telling you (and not telling you) is crucial for making better tuning decisions.
- Consistency, accuracy, and repeatability are key to a great tune.
Introduction: Understanding Measurement Techniques in Car Audio Tuning [0:06]
The video aims to clarify why different measurement techniques in car audio tuning lead to different results. It's not about proving one method is superior, but about understanding the impact of these differences on consistency, repeatability, and decision-making during tuning. The content addresses the common issue of measurements varying in the same car and tunes not translating as expected. The video will present real-time data using common measurement approaches, focusing on achieving consistency and accuracy for desired results.
Initial Setup and Summed Response [1:07]
The presenter shows a speedrun of a quick tune on his car using a generic curve and process to demonstrate speakers following targets on screen, time-aligned and phase-aligned for accurate testing. He connects the multiplexing to show the summed response of the left mid and left tweeter, both following a target and in time and polarity. Capturing the tweeter, mid, and their sum shows a nice bridge between the two, indicating the data is working according to the process.
Individual Microphone Responses [2:34]
The presenter breaks apart the five microphones from the multiplexing to show that each contributes a different RTA response. Despite a smooth summed response, individual microphones show significant variations. These microphones are spaced wider than a typical headspace, yet still exhibit considerable deviations due to the complex acoustic environment in a car.
Comparing Averaged and Individual Responses [3:58]
The presenter compares the multipplexed average response of the mid-range to the response from the far left top microphone, revealing a 3dB difference at 944 Hz. He also compares the average response to the center microphone, a typical placement for static microphones, showing a 9dB difference at 1.2kHz. These deviations occur even in a highly optimized car, suggesting even greater variations in less optimized setups like factory speaker locations.
Tuning to a Center Microphone and Comparing to Multiplexed Array [6:03]
The presenter tunes the center microphone to a center trace as if there were no body in the seat, then compares this to the multipplexed array of all five microphones. The multipplexed array shows a boost between 1.2 and 1.4 kHz and in the upper 2 kHz range, which doesn't match the center microphone's data. The center microphone shows dips in the lower mid-range and boosts in the 700-900 Hz range, indicating the multipplexed array provides a more accurate representation of what is heard due to capturing data from multiple points in the listening environment.
Individual Microphone Contributions to the Multiplexed Response [7:27]
The presenter shows the contribution of each of the five microphones to the multiplexed response, noting that the microphone used for tuning matches the multiplexed result perfectly. Commonalities exist between some microphones, like peaks in the mid 2kHz range for the yellow and gray traces. The presenter highlights the significant differences between individual microphones, showing over 8.5 dB difference at 1.2kHz.
Impact of Body Position on Microphone Measurements [9:11]
The presenter compares the center microphone's response with no body in the seat to the response with the presenter sitting in the seat holding the microphone. The presence of the body shifts a dip from 1.2kHz to 700-800Hz, demonstrating the significant impact of body position on measurements. This highlights the need to adjust targets based on whether someone is in the seat during tuning.
Moving Microphone Average [10:08]
The presenter uses a moving microphone average with the body in the seat, which smooths out the dip around 700-800 Hz compared to a static measurement. This shows that the moving mic method can clean up data and aid decision-making. However, differences persist, and changing the microphone's orientation alters the end result, underscoring the importance of microphone calibration files and understanding their behavior.
Measurements from the Back Seat [11:51]
The presenter takes measurements from the back seat with the microphone facing up, forward, and in a figure-eight motion. The figure-eight motion provides the most accurate representation compared to the multipplexed average. The figure-eight motion captures more data from the sides of the head, contributing to a better response.
Comparison of Figure-Eight Motion to Multiplexed Average [12:50]
The presenter compares the figure-eight measurement from the back seat to the multipplexed average without a body in the seat. While the two meet well on crossover slopes, differences remain around 1kHz and 1.5kHz, critical regions for vocals, guitars, and snare drums. This comparison emphasizes the importance of understanding how measurements relate to tuning decisions.
Understanding Measurements and Their Limitations [14:19]
Each microphone shows a dramatically different response due to physics, not measurement error, reflecting localized effects like comb filtering and off-axis behavior. Microphones don't interpret data; people do, making understanding measurements crucial. The common mistake is assuming a single static microphone represents what we hear, when it only shows dips caused by comb filtering at that specific location. Aggressively EQing these dips only fixes the microphone's response in that spot, not the system itself.
The Role of the Body in Acoustic Measurements [15:45]
Every sound we hear is influenced by our body's presence in the acoustic system. While tuning with a body isn't mandatory, understanding the changes it introduces is essential. Moving the microphone to create an average improves results, but consistency becomes a challenge due to variables like movement speed, range, and body interference.
Solutions for Repeatability and Accuracy [16:47]
For better repeatability with a single microphone, measuring from the back seat can reduce body interference, though it's still a compromise. Microphone arrays solve problems by averaging space, removing motion variables, and eliminating body shape variables. Arrays provide repeatable measurements and consistent optimization regardless of who sits in the seat.
Target Curves and Tuning Approaches [17:42]
Most target curves are designed around a single microphone or an array, not with a human body in the space. Tuning with a body present bakes the body's influence into the data, while tuning without a body treats it as a predictable filter. The latter approach is more repeatable across different cars and tuning sessions.
Simplification and Consistency [18:17]
Added variables like body and microphone position introduce uncertainty. The presenter's method prioritizes consistency, enabling meaningful comparisons and reliable results. While factors like temperature and humidity exist, they are secondary to the core principles. Consistency allows for meaningful comparison, repeatability, and reliable results.
Practical Benefits of Using an Array [18:57]
Besides speed and accuracy, using a microphone array reduces exposure to loud pink noise, allowing for ear use only in final refinement. Different measurement techniques produce different traces, making it nearly impossible to match data from different systems. The goal is to choose a method that is consistent, accurate, and repeatable for a great tune.
Key Takeaways and Conclusion [19:48]
Measurements don't exist in isolation; every method has assumptions, tradeoffs, and added variables that introduce uncertainty. There's no single correct way to measure or tune, but some methods are more consistent and repeatable. Focus on controlling variables, simplifying the measurement process, and using methods that allow for meaningful comparisons. Understanding what data is telling you (and not telling you) leads to better tuning decisions, reliable results, and great systems.
