Abstract
Acoustic scene classification is an intricate problem for a machine. As an emerging field of research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the classification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal. On the other hand, most of previous CNN-based audio scene classification approaches aim to improve the classification accuracy, by employing different regularization techniques, such as the dropout of hidden units and data augmentation, to reduce overfitting. It is widely known that outliers in the training set have a high negative influence on the trained model, and culling the outliers may improve the classification performance, while it is often under-explored in previous studies. In this paper, inspired by the silence removal in the speech signal processing, a novel sample dropout approach is proposed, which aims to remove outliers in the training dataset. Using the DCASE 2017 audio scene classification datasets, the experimental results demonstrates the proposed multi-scale DenseNet providing a superior performance than the traditional single-scale DenseNet, while the sample dropout method can further improve the classification robustness of multi-scale DenseNet.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Dixit, M., Chen, S., Gao, D., Rasiwasia, N., Vasconcelos, N.: Scene classification with semantic fisher vectors. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2974–2983. IEEE (2015)
Stowell, D., Giannoulis, D., Benetos, E., Lagrange, M., Plumbley, M.D.: Detection and classification of acoustic scenes and events. IEEE Trans. Multimedia 17(10), 1733–1746 (2015)
Eghbal-Zadeh, H., Lehner, B., Dorfer, M., Widmer, G.: CP-JKU submissions for DCASE-2016: a hybrid approach using binaural i-vectors and deep convolutional neural networks. In: IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) (2016)
Mesaros, A., Heittola, T., Virtanen, T.: TUT database for acoustic scene classification and sound event detection. In: 2016 24th European Signal Processing Conference (EUSIPCO), pp. 1128–1132. IEEE (2016)
Mesaros, A., et al.: DCASE 2017 challenge setup: tasks, datasets and baseline system. In: DCASE 2017-Workshop on Detection and Classification of Acoustic Scenes and Events (2017)
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)
Geiger, J.T., Schuller, B., Rigoll, G.: Large-scale audio feature extraction and SVM for acoustic scene classification. In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 1–4. IEEE (2013)
Fonseca, E., Gong, R., Bogdanov, D., Slizovskaia, O., Gómez Gutiérrez, E., Serra, X.: Acoustic scene classification by ensembling gradient boosting machine and convolutional neural networks. In: Virtanen, T., et al. (eds.) Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), 16 November 2017, Munich, Germany. Tampere (Finland): Tampere University of Technology, pp. 37–41. Tampere University of Technology (2017)
Aytar, Y., Vondrick, C., Torralba, A.: Soundnet: learning sound representations from unlabeled video. In: Advances in Neural Information Processing Systems, pp. 892–900 (2016)
Marchi, E., Tonelli, D., Xu, X., Ringeval, F., Deng, J., Schuller, B.: The up system for the 2016 DCASE challenge using deep recurrent neural network and multiscale kernel subspace learning. In: Detection and Classification of Acoustic Scenes and Events (2016)
Bae, S.H., Choi, I., Kim, N.S.: Acoustic scene classification using parallel combination of LSTM and CNN. In: Proceedings of the Detection and Classification of Acoustic Scenes and Events 2016 Workshop (DCASE2016), pp. 11–15 (2016)
Phan, H., Koch, P., Hertel, L., Maass, M., Mazur, R., Mertins, A.: CNN-LTE: a class of 1-x pooling convolutional neural networks on label tree embeddings for audio scene classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 136–140. IEEE (2017)
Xu, K., et al.: Mixup-based acoustic scene classification using multi-channel convolutional neural network. arXiv preprint arXiv:1805.07319 (2018)
Rakotomamonjy, A., Gasso, G.: Histogram of gradients of time-frequency representations for audio scene classification. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 23(1), 142–153 (2015)
Piczak, K.J.: ESC: dataset for environmental sound classification. In: Proceedings of the 23rd ACM International Conference on Multimedia, pp. 1015–1018. ACM (2015)
LeCun, Y., et al.: Learning algorithms for classification: a comparison on handwritten digit recognition. Neural Netw. Stat. Mech. Perspect. 261, 276 (1995)
Li, B., Xu, K., Cui, X., Wang, Y., Ai, X., Wang, Y.: Multi-scale DenseNet-based electricity theft detection. arXiv preprint arXiv:1805.09591 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, no. 2, p. 3 (2017)
Huang, G., Chen, D., Li, T., Wu, F., van der Maaten, L., Weinberger, K.Q.: Multi-scale dense convolutional networks for efficient prediction. arXiv preprint arXiv:1703.09844 (2017)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Acknowledgement
This study was supported by the Strategic Priority Research Programme (17-ZLXD-XX-02-06-02-08).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Feng, D., Xu, K., Mi, H., Liao, F., Zhou, Y. (2018). Sample Dropout for Audio Scene Classification Using Multi-scale Dense Connected Convolutional Neural Network. In: Yoshida, K., Lee, M. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2018. Lecture Notes in Computer Science(), vol 11016. Springer, Cham. https://doi.org/10.1007/978-3-319-97289-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-97289-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97288-6
Online ISBN: 978-3-319-97289-3
eBook Packages: Computer ScienceComputer Science (R0)