# Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
## 4 Results
Ideal**} & \multicolumn{2}{c}{**Simulation vs.**} \\  & \multicolumn{2}{c}{**Contour**} & \multicolumn{2}{c}{**Contour**} & \multicolumn{2}{c}{**Experimental**} \\ \cline{2-7}  & **Corr** & **RMSE** & **Corr** & **RMSE** & **Corr** & **RMSE** \\ \hline
1 & 0.9315 & 3.17 & 0.9187 & 2\_.61 & 0.9846 & 1.07 \\ \hline
2 & 0.9076 & 3.18 & 0.9302 & 5.09 & 0.9636 & 2.42 \\ \hline
3 & 0.9106 & 2.49 & 0.894 &  
The Pearson correlation analysis and root mean square error (RMSE) calculations used in previous analyses were also applied to the simulation model and experimental setup after optimization. The correlation coefficient between the experimental and simulation results was calculated to be 0.9864, and the RMSE value was 1.00. According to the results of the analysis, the simulation and experimental results showed a high degree of parallelism, indicating that the simulation model is consistent with the experimental data and demonstrating the reliability of the model. Subsequently, the simulation and experimental results were compared with the ideal contour. The correlation coefficient between the ideal contour and the simulation results was found to be 0.9692, with an RMSE value of 1.17. The correlation coefficient between the ideal contour and the experimental results was calculated to be 0.9675, with an RMSE value of 1.34. These results indicate that the filter parameters obtained after optimization provide a high similarity to the ideal contour. The correlation coefficients approaching 1 indicate that both the simulation and experimental results have a strong linear relationship with the ideal contour after optimization. This demonstrates an increase in the accuracy and reliability of the model. The decrease in RMSE values shows that the filter parameters obtained after optimization yield close \begin{table}
\begin{tabular}{c c c} \hline
**Center Frequency Values (Hz)** & **Gain Values (dB)** & **Q Factor Values** \\ \hline
58 & 11.8 & 0.9 \\ \hline
63 & 11.9 & 0.7 \\ \hline
157 & \(-\)0.5 & 1.0 \\ \hline
338 & \(-\)3.2 & 0.8 \\ \hline
589 & 0.7 & 0.8 \\ \hline
894 & 6.2 & 1.1 \\ \hline
1163 & 7.1 & 0.9 \\ \hline
3326 & \(-\)11.7 & 0.9 \\ \hline
6768 & 12.1 & 1.5 \\ \hline
7792 & 11.3 & 1.6 \\ \hline \end{tabular}
\end{table} 
Table 7: Parametric equalizer filter parameters obtained from optimization in run 9. Figure 10: Comparison of simulation, experimental, and equal-loudness contour for speaker output obtained with optimized filter parameters. results to the ideal contour, and that amplitude deviations are minimized. Low RMSE values indicate a significant improvement in system performance and a reduction in the model's error margin. These results also demonstrate a significant improvement in the performance of the in-vehicle audio system. By bringing the speaker output closer to the ideal contour through optimization, a higher quality and more comfortable audio experience for drivers and passengers has been achieved. This shows that genetic algorithm-based optimization is an effective method for optimizing the frequency response of in-vehicle audio systems and for enhancing system performance in real-world applications.