Loudspeaker System Identification Based on Adaptive Gradient Descent Algorithm

In loudspeaker model parameter identification, the conventional fixed-step gradient descent algorithm is time-consuming and often unstable when the initial parameter error is large. Therefore, a variable-step gradient descent algorithm for identifying speaker system parameters in the frequency domain is proposed. The adaptive method monitors the trend of the parameter identification process and adaptively adjusts the corresponding learning rate, eliminating the need for manual adjustment. Additionally, since directly calculating the gradient of a complex model is challenging, a central difference method is employed to approximate the model's gradient. By establishing a dynamic loudspeaker model and setting different initial values and iteration error termination criteria, the convergence and identification performance of the fixed-step method, least squares method, and adaptive-step method are compared. Micro loudspeakers are used for testing and verification. Simulations and experiments demonstrate that the proposed method has higher efficiency and better robustness to initial errors, exhibiting superior adaptability and universality.