# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 5 Conclusions
These metrics indicate that the filter parameters optimized in this run provided the highest similarity to the ideal contour and demonstrated the best agreement between the simulation and the experimental results. When comparing the updated results to the initial values, the Pearson correlation coefficient for the simulation results improved from 0.6341 to 0.9692, a significant improvement of 52.84%. The RMSE value for the simulation results decreased from 4.88 to 1.17, showing an improvement of 76.02%. Similarly, the Pearson correlation coefficient for the experimental results increased from 0.6715 to 0.9675, an improvement of 44.08%, while the RMSE value decreased from 2.57 to 1.34, an improvement of 47.86%. These improvements not only facilitate the real-world application of this study but also allow for making finer adjustments. The results obtained after optimization indicate that the speaker output has been brought closer to the ideal contour, significantly improving the performance of the in-vehicle audio system. Both the simulation and experimental results demonstrate that the optimization effectively aligns the speaker response with the ideal contour, thereby providing a higher quality and more comfortable audio experience for drivers and passengers. These findings show that genetic algorithm-based optimization is an effective method for optimizing the frequency response of in-vehicle audio systems and enhancing system performance in real-world applications. The proposed genetic algorithm-based approach offers several advantages over traditional methods. By focusing on the ideal hearing curve defined by standards, our method ensures a more accurate alignment with human auditory perception. Unlike most existing methods, which optimize filters based on user satisfaction or specific frequency responses, our approach provides a comprehensive evaluation, using both RMSE and correlation metrics. This dual-metric analysis allows for a more thorough assessment of the similarity between the optimized frequency response and the ideal curve, resulting in a higher quality audio experience. However, there are also some limitations to our approach. The genetic algorithm can be computationally intensive, especially with large population sizes and many generations, which can lead to longer processing times. Additionally, fine-tuning the genetic algorithm parameters requires expertise and can be complex. The stochastic nature of genetic algorithms can also result in variability in the outcomes, necessitating multiple runs to ensure robustness and reliability. Furthermore, the need for extensive simulations and experiments to validate the algorithm's effectiveness can be resource-intensive. These findings underscore the significance and innovation of our approach in enhancing in-vehicle audio performance. Future work will focus on further refining the algorithm parameters and exploring additional optimization techniques to enhance the robustness and efficiency of the proposed method. In future studies, it is recommended to better understand and include in the modeling some factors that were not considered in this study, such as the frequency responses of cables, the vehicle multimedia system, and the audio analyzer. Additionally, it is recommended that modeling and optimization studies be planned using a genetic algorithm to bring the ideal contour closer to the head region of the driver's seat. In conclusion, this study demonstrates the effectiveness of a genetic algorithm-based approach for optimizing the speaker output according to the equal-loudness contour. It shows the potential to achieve higher performance and users' satisfaction in in-vehicle audio systems. **Author Contributions:** Conceptualization, V.B., O.C. and G.Y. ; methodology, V.B., O.C. and G.Y. ; software, V.B. and O.C. ; validation, V.B. and O.C. ; formal analysis, V.B., O.C. and G.Y. ; investigation, V.B. and O.C. ; resources, V.B. ; data curation, V.B. and O.C. ; writing--original draft preparation, V.B., O.C. and G.Y.