Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles

Abstract

To improve the diversity and quality of sound mimicry of electric automobile engines, a generative adversarial network (GAN) model was used to construct an active sound production model for electric automobiles. The structure of each layer in the network in this model and the size of its convolution kernel were designed. The gradient descent in network training was optimized using the adaptive moment estimation (Adam) algorithm. To demonstrate the quality difference of the generated samples from different input signals, two GAN models with different inputs were constructed. The experimental results indicate that the model can accurately learn the characteristic distributions of raw audio signals. Results from a human ear auditory test show that the generated audio samples mimicked the real samples well, and a leave-one-out (LOO) test show that the diversity of the samples generated from the raw audio signals was higher than that of samples generated from a two-dimensional spectrogram.