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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings
Pages167-175
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2011 Jun 24
Externally publishedYes
Event21st International Conference on Artificial Neural Networks, ICANN 2011 - Espoo, Finland
Duration: 2011 Jun 142011 Jun 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6791 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

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

Keywords

  • Classification
  • Environmental Sounds
  • Neuro-dynamical Model
  • Prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhang, Y., Nishide, S., Takahashi, T., Okuno, H. G., & Ogata, T. (2011). Cluster self-organization of known and unknown environmental sounds using recurrent neural network. In Artificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings (PART 1 ed., pp. 167-175). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6791 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21735-7_21