Along with the development of the technology of electric vehicles using a motor instead of an internal combustion engine, research on the active acoustic design for generating a vehicle driving sound that is desired by users is being actively conducted. Therefore, the need to quantitatively evaluate effective sound quality characteristics of a vehicle according to existing expert judgment, through a virtual model such as a deep learning-based algorithm has increased. Deep learning-based algorithms can improve the accuracy of prediction when there is a large amount of learning data; however, obtaining such data in actual industrial sites is difficult. In this study, we presented a change in the accuracy of a deep learning-based algorithm based on the number of data augmentation. Additionally, we verified the basis of the algorithm's judgment by implementing the explainable artificial intelligence technique.
|Translated title of the contribution||Classification of Affective Sound Quality Characteristics of Spectrogram-based Vehicle Driving Sounds Using Data Augmentation|
|Number of pages||8|
|Journal||Transactions of the Korean Society of Mechanical Engineers, A|
|Publication status||Published - 2022|
Bibliographical notePublisher Copyright:
© 2022 The Korean Society of Mechanical Engineers.
All Science Journal Classification (ASJC) codes
- Mechanical Engineering