Abstract
In this paper, the author proposes to use PG-LSTM (physics guided long short-term memory neural network) to model the circuit and mechanical structure of the micro-loudspeaker to improve the accuracy of the diaphragm displacement prediction. Compared with conventional physical model, the PG-LSTM model takes into account the influence of temperature on the loudspeaker system, and reduces the displacement prediction errors caused by system nonlinear factors when the input signal amplitude is large. The experimental results show that the mean-squared-error and maxim-absolute-error of the proposed model is reduced by 44% and 53% compared to the conventional physical model respectively.
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