LEARNING
An Acoustic Events Recognition for Robotic Systems Based on a Deep Learning Method
Tadaaki Niwa, Takashi Kawakami, Ryosuke Ooe, Tamotsu Mitamura, Masahiro Kinoshita, Masaaki Wajima
- Year
- 2015
- Citations
- 2
- Access
- Open access
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.
Keywords
Computer scienceArtificial intelligenceWaveformDeep learningRobotDeep neural networksSpeech recognitionTelecommunications
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