Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44381
標題: Birdsong recognition using prediction-based recurrent neural fuzzy networks
作者: Juang, C.F.
莊家峰
Chen, T.M.
關鍵字: bird species identification
temporal sequence prediction
neural
networks
recurrent fuzzy systems
LPC coefficients
systems
identification
期刊/報告no:: Neurocomputing, Volume 71, Issue 1-3, Page(s) 121-130.
摘要: Automatic birdsong recognition using prediction-based singleton-type recurrent neural fuzzy networks (SRNFNs) is proposed in this paper. The recognition task consists of two stages. The first stage segments a significant portion from a birdsong sequence and the second stage performs recognition. For birdsong segmentation, an easy but effective segmentation approach based on time domain energy is proposed. For recognition, the linear predictive coding (LPC) coefficients of each frame in a segmented birdsong are extracted and used as features. These features are fed as inputs to SRNFN recognizers. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and its recurrent aspect makes it suitable for processing patterns with temporal characteristics. In birdsong recognition, the sample prediction technique is used, where one SRNFN is responsible for learning the temporal birdsong relationships of only one species. The prediction error of each SRNFN is then used as a criterion for recognition. Experiments with 10 species of birds and their songs are performed, and a high recognition rate is achieved. Comparisons with a Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network (TRFN) and backpropagation neural network are also made in the experiments. (c) 2007 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/11455/44381
ISSN: 0925-2312
文章連結: http://dx.doi.org/10.1016/j.neucom.2007.08.011
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