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|Title:||Classification of Known and Unknown Environmental Sounds Based on Self-Organized Space Using a Recurrent Neural Network|
Okuno, Hiroshi G.
|Author's alias:||尾形, 哲也|
|Keywords:||Recurrent Neural Network|
|Publisher:||Brill Academic Publishers|
|Journal title:||Advanced Robotics|
|Abstract:||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.|
|Rights:||© Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2011|
This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。
|Appears in Collections:||Journal Articles|
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