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Masked Conditional Neural Networks for Environmental Sound Classification

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Artificial Intelligence XXXIV (SGAI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10630))

Abstract

The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by embedding a filterbank-like sparseness over the network’s links using a binary mask. Additionally, the masking automates the exploration of different feature combinations concurrently analogous to handcrafting the optimum combination of features for a recognition task. We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds. Additionally, we present a collection of manually recorded sounds for rail and road traffic, YorNoise, to investigate the confusion rates among machine generated sounds possessing low-frequency components. MCLNN has achieved competitive results without augmentation and using 12% of the trainable parameters utilized by an equivalent model based on state-of-the-art Convolutional Neural Networks on the Urbansound8k. We extended the Urbansound8k dataset with YorNoise, where experiments have shown that common tonal properties affect the classification performance.

This work is funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 608014 (CAPACITIE).

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Notes

  1. 1.

    https://keras.io.

  2. 2.

    http://deeplearning.net/software/theano.

  3. 3.

    https://librosa.github.io/.

  4. 4.

    http://ffmpeg.org/.

  5. 5.

    https://github.com/fadymedhat/YorNoise.

References

  1. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  2. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)

    Article  Google Scholar 

  3. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT (1992)

    Google Scholar 

  4. Dieleman, S., Schrauwen, B.: End-to-end learning for music audio. In: International Conference on Acoustics, Speech and Signal Processing, ICASSP (2014)

    Google Scholar 

  5. Fahlman, S.E., Hinton, G.E., Sejnowski, T.J.: Massively parallel architectures for Al: NETL, Thistle, and Boltzmann machines. In: National Conference on Artificial Intelligence, AAAI (1983)

    Google Scholar 

  6. Hamel, P., Eck, D.: Learning features from music audio with deep belief networks. In: International Society for Music Information Retrieval Conference, ISMIR (2010)

    Google Scholar 

  7. Taylor, G.W., Hinton, G.E., Roweis, S.: Modeling human motion using binary latent variables. In: Advances in Neural Information Processing Systems, NIPS, pp. 1345–1352 (2006)

    Google Scholar 

  8. Battenberg, E., Wessel, D.: Analyzing drum patterns using conditional deep belief networks. In: International Society for Music Information Retrieval, ISMIR (2012)

    Google Scholar 

  9. Mohamed, A.-R., Hinton, G.: Phone recognition using restricted boltzmann machines In: IEEE International Conference on Acoustics Speech and Signal Processing, ICASSP (2010)

    Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Neural Information Processing Systems, NIPS (2012)

    Google Scholar 

  11. Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22, 1533–1545 (2014)

    Article  Google Scholar 

  12. Pons, J., Lidy, T., Serra, X.: Experimenting with musically motivated convolutional neural networks. In: International Workshop on Content-based Multimedia Indexing, CBMI (2016)

    Google Scholar 

  13. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  14. Choi, K., Fazekas, G., Sandler, M., Cho, K.: Convolutional recurrent neural networks for music classification. In: arXiv preprint arXiv:1609.04243 (2016)

  15. Lee, H., Grosse, R. Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML, pp. 1–8 (2009)

    Google Scholar 

  16. Lee, H., Largman, Y., Pham, P., Ng, A.Y.: Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Neural Information Processing Systems (NIPS) (2009)

    Google Scholar 

  17. Medhat, F., Chesmore, D., Robinson, J.: Masked conditional neural networks for audio classification. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2017. LNCS, vol. 10614, pp. 349–358. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68612-7_40

    Chapter  Google Scholar 

  18. Medhat, F., Chesmore, D., Robinson, J.: Masked conditional neural networks for automatic sound events recognition. In: IEEE International Conference on Data Science and Advanced Analytics (DSAA) (2017)

    Google Scholar 

  19. Lin, M., Chen, Q., Yan, S.: Network in network. In: International Conference on Learning Representations, ICLR (2014)

    Google Scholar 

  20. Bergstra, J., Casagrande, N., Erhan, D., Eck, D., Kégl, B.: Aggregate features and AdaBoost for music classification. Mach. Learn. 65, 473–484 (2006)

    Article  Google Scholar 

  21. Salamon, J., Jacoby, C., Bello, J.P.: A dataset and taxonomy for urban sound research. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 1041–1044 (2014)

    Google Scholar 

  22. He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision, ICCV (2015)

    Google Scholar 

  23. Kingma, D., Ba, J.: ADAM: a method for stochastic optimization. In: International Conference for Learning Representations, ICLR (2015)

    Google Scholar 

  24. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. JMLR 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  25. Salamon, J., Bello, J.P.: Unsupervised feature learning for urban sound classification. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2015)

    Google Scholar 

  26. Piczak, K.J.: Environmental sound classification with convolutional neural networks. In: IEEE International Workshop on Machine Learning For Signal Processing (MLSP) (2015)

    Google Scholar 

  27. Salamon, J., Bello, J.P.: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett. 24, 279–283 (2017)

    Article  Google Scholar 

  28. Dhillon, I.S., Modha, D.S.: Concept decompositions for large sparse text data using clustering. Mach. Learn. 42, 143–175 (2001)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electronic Engineering, University of York, York, UK

    Fady Medhat, David Chesmore & John Robinson

Authors
  1. Fady Medhat
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  2. David Chesmore
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  3. John Robinson
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Corresponding author

Correspondence to Fady Medhat .

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Editors and Affiliations

  1. University of Portsmouth, Portsmouth, United Kingdom

    Max Bramer

  2. Middlesex University , London, United Kingdom

    Miltos Petridis

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Medhat, F., Chesmore, D., Robinson, J. (2017). Masked Conditional Neural Networks for Environmental Sound Classification. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science(), vol 10630. Springer, Cham. https://doi.org/10.1007/978-3-319-71078-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-71078-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71077-8

  • Online ISBN: 978-3-319-71078-5

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