Abstract
BSR (Buzz, squeak, and rattle) noises are essential criteria for the quality of a vehicle. It is necessary to classify them to handle them appropriately. Although many studies have been conducted to classify noise, they suffered some problems: the difficulty in extracting features, a small amount of data to train a classifier, and less robustness to background noise. This paper proposes a method called transferred encoder-decoder generative adversarial networks (tedGAN) which solves the problems. Deep auto-encoder (DAE) compresses and reconstructs the audio data for capturing the features of them. The decoder network is transferred to the generator of GAN so as to make the process of training generator more stable. Because the generator and the discriminator of GAN are trained at the same time, the capacity of extracting features is enhanced, and a knowledge space of the data is expanded with a small amount of data. The discriminator to classify whether the input is the real or fake BSR noises is transferred again to the classifier; then it is finally trained to classify the BSR noises. The classifier yields the accuracy of 95.15%, which outperforms other machine learning models. We analyze the model with t-SNE algorithm to investigate the misclassified data. The proposed model achieves the accuracy of 92.05% for the data including background noise.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mattingly-Hannigan, E.: Vibration Testing on the Job, pp. 55–57. Mattingly Publishing Co. Inc. (2008)
Mog, M.G., Min, B.H., Chio, S.W., Lee, H.J.: Development of the reproduction test method of automobile buzz, squeak, rattle noise and the noise tracking system. In: Korean Society of Automotive Engineers (KSAE), pp. 1475–1481 (2010)
Saki, F., Kehtarnavaz, N.: Background noise classification using random forest tree classifier for cochlear implant applications. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3591–3595. IEEE (2014)
Li, Y., Li, Y.: Eco-environmental sound classification based on matching pursuit and support vector machine. In: 2010 2nd International Conference on Information Engineering and Computer Science (ICIECS), pp. 1–4. IEEE (2010)
Wang, J.C., Lin, C.H., Chen, B.W., Tsai, M.K.: Gabor-based nonuniform scale-frequency map for environmental sound classification in home automation. IEEE Trans. Autom. Sci. Eng. 11(2), 607–613 (2014)
Amiriparian, S., Gerczuk, M., Ottl, S., Cummins, N., Freitag, M., Pugachevskiy, S., Baird, A., Schuller, B.: Snore sound classification using image-based deep spectrum features. In: Proceedings of Interspeech, vol. 17, pp. 3512–3516 (2017)
Lee, J., Lee, S., Kwak, Y., Kim, B., Park, J.: Temporal and spectral characteristics of BSR noises and influence on auditory perception. J. Mech. Sci. Technol. 29(12), 5199–5204 (2015)
Salamon, J., Bello, J.P.: Unsupervised feature learning for urban sound classification. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 171–175. IEEE (2015)
Deng, L., Li, J., Huang, J.T., Yao, K., Yu, D., Seide, F., Seltzer, M.L., Zweig, G., He, X., Williams, J., Gong, Y., Acero, A.: Recent advances in deep learning for speech research at Microsoft. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8604–8608. IEEE (2013)
Tanweer, S., Mobin, A., Alam, A.: Environmental noise classification using LDA, QDA and ANN methods. Indian J. Sci. Technol. 9(33), 1–8 (2016)
Abdul Rahim, N., Paulraj, M.P., Adom, A.H., Shukor, S.A.A., Masnan, M.J.: Homogeneous multi-classifier system for moving vehicles noise classification based on multilayer perceptron. J. Intell. Fuzzy Syst. 29(1), 149–157 (2015)
Piczak, K.J.: Environmental sound classification with convolutional neural networks. In: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2015)
Medhat, F., Chesmore, D., Robinson, J.: Masked conditional neural networks for environmental sound classification. In: Bramer, M., Petridis, M. (eds.) SGAI-AI 2017. LNCS (LNAI), vol. 10630, pp. 21–33. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71078-5_2
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Krizhevsky, A., Hinton, G.E.: Using very deep autoencoders for content-based image retrieval. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 489–494 (2011)
Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: International Conference on Machine Learning, pp. 1096–1103 (2008)
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Goodfellow, I., Pouget-Abadie, J., Mirze, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). arXiv preprint: arXiv:1511.06434
Arnold, A., Nallapati, R., Cohen, W.: A comparative study of methods for transductive transfer learning. In: IEEE International Conference on Data Mining, pp. 77–82 (2007)
Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Acknowledgement
This work has been supported by a grant from Hyundai motors, Inc.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Kim, JY., Bu, SJ., Cho, SB. (2018). Hybrid Deep Learning Based on GAN for Classifying BSR Noises from Invehicle Sensors. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)