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.