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

Author(s): Shafi Imran | Ahmad Jamil | Shah SyedIsmail | Ikram AtaulAziz | Ahmad Khan Adnan | Bashir Sajid

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2010;
Issue: 1;
Start page: 636858;
Date: 2010;
Original page

Abstract 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.

Tango Jona
Tangokurs Rapperswil-Jona

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