Deep CNN transferred from VAE and GAN for classifying irritating noise in automobile

Jin Young Kim, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Noise from automobiles, such as buzzing, squeaking, and rattling (BSR) noises, is a key factor in automobile quality assessment. It is necessary to classify these noises for appropriate handling and prevention. Although many researchers have conducted studies to classify noise, they suffer from several problems: difficulty in extracting appropriate features, insufficient data to train a classifier, and weak robustness to surrounding noise. This paper proposes a method called latent semantic controlling generative adversarial networks (LSC-GAN) to solve these problems. To capture the features of data, a variational autoencoder (VAE), an autoencoder with approximate inference in a latent Gaussian model, learns the data representation by projecting them into the latent space according to their features and reconstructing the projected data. Because the generator and the discriminator of the LSC-GAN are trained simultaneously, the capacity to extract the characteristics of the data is improved and a knowledge space of classifiable data is also expanded with insufficient data. While data are generated by the generator, the encoder projects them back to the latent space according to their characteristics to advance the ability to extract features. Finally, the encoder is trained to the classifier, which is trained to classify BSR noises. The proposed classifier outperforms other models and achieves an accuracy of 96.68%. We confirm using a confusion matrix that the proposed model classifies the types of insufficient class better than other models. Our proposed model classifies data with accuracy of 94.68%, even if the data contains surrounding noise, which means it is more robust to BSR with surrounding noise than other models.

Original languageEnglish
Pages (from-to)395-403
Number of pages9
Publication statusPublished - 2021 Sept 10

Bibliographical note

Funding Information:
This work was partially supported by Korea Electric Power Corporation (Grant number: R18XA05 ) and an Institute of Information & Communications Technology Planning & Evaluation ( IITP ) grant funded by the Korean government ( MSIT ) (No. 2020-0-01361 , Artificial Intelligence Graduate School Program ( Yonsei University )). J.-Y. Kim has been supported by NRF ( National Research Foundation of Korea ) grant funded by the Korean government (NRF-2019-Fostering Core Leaders of the Future Basic Science Program/Global Ph.D. Fellowship Program).

Publisher Copyright:
© 2020 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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