An Acoustic Events Recognition for Robotic Systems Based on a Deep Learning Method


In this paper, we provide a new approach to classify and recognize the acoustic events for multiple autonomous robots systems based on the deep learning mechanisms. For disaster response robotic systems, recognizing certain acoustic events in the noisy environment is very effective to perform a given operation. As a new approach, trained deep learning networks which are constructed by RBMs, classify the acoustic events from input waveform signals. From the experimental results, usefulness of our approach is discussed and verified.

Share and Cite:

Niwa, T. , Kawakami, T. , Ooe, R. , Mitamura, T. , Kinoshita, M. and Wajima, M. (2015) An Acoustic Events Recognition for Robotic Systems Based on a Deep Learning Method. Journal of Computer and Communications, 3, 46-51. doi: 10.4236/jcc.2015.311008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Hinton, G.E., Osindero, S. and Teh, Y.W. (2006) A Fast Learning Algorithm for Deep Belief Nets. Neural computation, 18, 1527-1554.
[2] Freund, Y. and Haussler, D. (1994) Unsupervised Learning of Distributions of Binary Vectors Using Two Layer Networks. Computer Research Laboratory [University of California, Santa Cruz].
[3] Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, 313, 504-507.
[4] Cho, K., Ilin, A. and Raiko, T. (2011) Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines. In: Artificial Neural Networks and Machine Learning—ICANN 2011, Springer Berlin Heidelberg, 10-17.
[5] Hinton, G.E. (2002) Training Products of Experts by Minimizing Contrastive Divergence. Neural Computation, 14, 1771-1800.
[6] Norouzi, M., Ranjbar, M. and Mori, G. (2009) Stacks of Convo-lutional Restricted Boltzmann Machines for Shift-In- variant Feature Learning. IEEE Conference on Computer Vision and Pattern Recognition, 2735-2742.
[7] Lee, H., Grosse, R., Ranganath, R. and Ng, A.Y. (2009) Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. Proceedings of the 26th Annual International Conference on Machine Learning, 609-616.
[8] Simard, P.Y., Steinkraus, D. and Platt, J.C. (2003) Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis. In: null, 958.
[9] Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) Imagenet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 1097-1105.
[10] Kingma, D. and Ba, J. (2015) Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015).
[11] IEEE AASP Challenge: Detection and Classification of Acoustic Scenes and Events.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.