NDLI: Long-term learning behavior in a recurrent neural network for sound recognition

Content Provider	IEEE Xplore Digital Library
Author	Boes, M. Oldoni, D. De Coensel, B. Botteldooren, D.
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. Technol., Ghent Univ., Ghent, Belgium (Boes, M.; Oldoni, D.; De Coensel, B.; Botteldooren, D.)
Abstract	In this paper, the long-term learning properties of an artificial neural network model, designed for sound recognition and computational auditory scene analysis in general, are investigated. The model is designed to run for long periods of time (weeks to months) on low-cost hardware, used in a noise monitoring network, and builds upon previous work by the same authors. It consists of three neural layers, connected to each other by feedforward and feedback excitatory connections. It is shown that the different mechanisms that drive auditory attention emerge naturally from the way in which neural activation and intra-layer inhibitory connections are implemented in the model. Training of the artificial neural network is done following the Hebb principle, dictating that "Cells that fire together, wire together", with some important modifications, compared to standard Hebbian learning. As the model is designed to be on-line for extended periods of time, also learning mechanisms need to be adapted to this. The learning needs to be strongly attention- and saliency-driven, in order not to waste available memory space for sounds that are of no interest to the human listener. The model also implements plasticity, in order to deal with new or changing input over time, without catastrophically forgetting what it already learned. On top of that, it is shown that also the implementation of short-term memory plays an important role in the long-term learning properties of the model. The above properties are investigated and demonstrated by training on real urban sound recordings.
Starting Page	3116
Ending Page	3123
File Size	2859325
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889658
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Biological system modeling Computational modeling Brain modeling Correlation Adaptation models Hebbian theory
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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