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.
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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: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6791. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21735-7_21
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DOI: https://doi.org/10.1007/978-3-642-21735-7_21
Publisher Name: Springer, Berlin, Heidelberg
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