Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm

Abstract

This study aims to automate the optimization of a full-range speaker in an SUV’s audio system according to the equal-loudness contours principle. The input signal and frequency responses of the amplifier and speaker were transferred to Matlab. Using ideal filter parameters, ten parametric equalizer models were created, and the speaker output was obtained using the convolution technique. The same filter settings were applied to the vehicle multimedia system, and the experimental results were obtained. The simulation and experimental results were compared, showing high similarity, with a Pearson correlation of 0.9295 and a root mean square error (RMSE) of 2.29. The results were compared with the ideal contour. The Pearson correlation coefficients for the simulation and experimental results were 0.6341 and 0.6715, with RMSE values of 4.88 and 2.57, showing low similarity. Consequently, each parametric equalizer filter’s parameters were optimized using a genetic algorithm. The genetic algorithm was executed thirteen times for robustness, and the best parameters were selected. The optimized parameters were applied to the multimedia system, and the results were compared with the ideal contour. The correlation coefficients for the simulation and experimental results were 0.9692 and 0.9675, with RMSE values of 1.17 and 1.34. These results indicate that optimization aligns the speaker output closer to the ideal contour, enhancing in-vehicle audio system performance and increasing users’ satisfaction.