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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.

has been cited by the following article:

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

  AUTHORS: Tadaaki Niwa, Takashi Kawakami, Ryosuke Ooe, Tamotsu Mitamura, Masahiro Kinoshita, Masaaki Wajima

  KEYWORDS: Acoustic Events Recognition, Deep Learning, Restricted Boltzmann Machine

  JOURNAL NAME: Journal of Computer and Communications, Vol.3 No.11, November 19, 2015

  ABSTRACT: 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.