# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 1 Introduction
However, these steps are detailed and challenging due to their reliance on manual tuning and experimental methods and because repeated analyses are required to achieve optimal results. Additionally, the process of evaluating results through subjective observation makes it difficult to ensure the consistency in achieving desired acoustic levels [6]. Given that the equal-loudness contour spans a wide frequency range, multiple parametric equalizer filters are often used in vehicle multimedia systems for fine-tuning. Considering that each filter is controlled by three parameters, the optimization of in-car sound systems becomes a particularly cumbersome process, especially for different types of speakers. Therefore, the use of computer-aided modeling and simulation technologies for the optimization of multiple parametric equalizer filters holds the potential to enhance the efficiency and effectiveness of the design process of in-car sound systems. Particularly, the filter design possibilities provided by computer-aided mathematical modeling software, coupled with simulations conducted through signal processing techniques to calculate the system's frequency response, and the assessment of conformity to the equal-loudness contour through statistical methods, both accelerate and simplify the optimization process of in-car sound systems. These modern approaches overcome the time and complexity issues encountered with experimental methods, offering the opportunity to perform detailed analyses necessary for perfecting the design and settings of in-car sound systems [7]. In addition to the previous processes, the use of intelligent algorithms during the tuning of filter parameters holds the potential to advance the optimization process. Methods such as metaheuristic algorithms and Artificial Neural Networks can be utilized to optimize the process of finding the most suitable filter combination for the equal-loudness contour, providing high levels of accuracy and efficiency in parameter selection. These intelligent algorithms, compared to manual trial-and-error methods, allow for the rapid and cost effective achievement of the ideal frequency response, reducing time and resource usage in acoustic tuning [8]. This study aims to automate the process of adjusting the full-range speaker in the audio system of an SUV according to the equal-loudness principle. First, the input sound and the frequency responses of the speaker were transferred to the Matlab environment. Then, using the filter parameter values defined in the standards for the ideal hearing curve in \(1/1\) octave bands, ten parametric equalizers were modeled, and the speaker output was obtained using the convolution technique. The same filter settings were applied to the vehicle's multimedia system, and the simulations were verified, obtaining experimental results. Following this step, the center frequency, gain, and Q factor of each parametric equalizer were determined using a genetic algorithm to achieve optimal acoustic performance. These filter parameters were iteratively optimized to match the ideal equal-loudness contour closely. After the optimization process, the calculated parameters were applied to the vehicle multimedia system, and the obtained experimental results were compared with the simulation results in the Matlab environment, using correlation and root mean square error analyses. A total of thirteen different experiments were conducted, and the results of each experiment were analyzed separately. This allowed for a detailed examination of the effectiveness of the modeling, simulation, and applied optimization strategy.