Validity-Guided Fuzzy Clustering Evaluation for Neural Network-Based Time-Frequency Reassignment

This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks (LNNs) used for reassigning time-frequency representations (TFRs). Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2010-01, Vol.2010 (1), Article 636858
Main Authors: Shafi, Imran, Ahmad, Jamil, Shah, SyedIsmail, Ikram, AtaulAziz, Ahmad Khan, Adnan, Bashir, Sajid
Format: Article
Language:English
Subjects:
Engineering
Quantum Information Technology
Research Article
Signal,Image and Speech Processing
Spintronics
Time-Frequency Analysis and Its Applications to Multimedia Signals
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Summary:This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks (LNNs) used for reassigning time-frequency representations (TFRs). Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number of clusters and in conjunction with neural network-based processing technique utilizing a hybrid approach can effectively reduce the blur in the spectrograms. In the course of every partitioning problem the number of subsets must be given before the calculation, but it is rarely known apriori, in this case it must be searched also with using validity measures. Experimental results demonstrate the effectiveness of the approach.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1155/2010/636858