Classification of known and unknown environmental sounds based on self-organized space using a recurrent neural network

Yang Zhang, Tetsuya Ogata*, Shun Nishide, Toru Takahashi, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Article査読

2 被引用数 (Scopus)

抄録

Our goal is to develop a system to learn and classify environmental sounds for robots working in the real world. In the real world, two main restrictions pertain in learning. (i) Robots have to learn using only a small amount of data in a limited time because of hardware restrictions. (ii) The system has to adapt to unknown data since it is virtually impossible to collect samples of all environmental sounds. We used a neuro-dynamical model to build a prediction and classification system. This neuro-dynamical model can self-organize sound classes into parameters by learning samples. The sound classification space, constructed by these parameters, is structured for the sound generation dynamics and obtains clusters not only for known classes, but also unknown classes. The proposed system searches on the basis of the sound classification space for classifying. In the experiment, we evaluated the accuracy of classification for both known and unknown sound classes.

本文言語English
ページ(範囲)2127-2141
ページ数15
ジャーナルAdvanced Robotics
25
17
DOI
出版ステータスPublished - 2011
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用
  • ハードウェアとアーキテクチャ
  • コンピュータ サイエンスの応用

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