Cluster self-organization of known and unknown environmental sounds using recurrent neural network

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

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Our goal is to develop a system that is able to learn and classify environmental sounds for robots working in the real world. In the real world, two main restrictions pertain in learning. First, the system has to learn using only a small amount of data in a limited time because of hardware restrictions. Second, it 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 which can self-organize sound classes into parameters by learning samples. The proposed system searches space of parameters for classifying. In the experiment, we evaluated the accuracy of classification for known and unknown sound classes.

本文言語English
ホスト出版物のタイトルArtificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings
ページ167-175
ページ数9
PART 1
DOI
出版ステータスPublished - 2011
外部発表はい
イベント21st International Conference on Artificial Neural Networks, ICANN 2011 - Espoo, Finland
継続期間: 2011 6 142011 6 17

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
番号PART 1
6791 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference21st International Conference on Artificial Neural Networks, ICANN 2011
国/地域Finland
CityEspoo
Period11/6/1411/6/17

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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