Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-28
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liang, Kai& Zhao, Haijun. 2020. Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles. Shock and Vibration،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1213179
Modern Language Association (MLA)
Liang, Kai& Zhao, Haijun. Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles. Shock and Vibration No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1213179
American Medical Association (AMA)
Liang, Kai& Zhao, Haijun. Application of Generative Adversarial Nets (GANs) in Active Sound Production System of Electric Automobiles. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1213179
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213179