Validity-Guided Fuzzy Clustering Evaluation for Neural Network-Based Time-Frequency Reassignment. (11th August 2010)

• Record Type: Journal Article
• Authors: Shafi Shafi, Imran Imran; Ahmad Ahmad, Jamil Jamil; Shah Shah, Syed Ismail Syed Ismail; Ikram Ikram, Ataul Aziz Ataul Aziz; Ahmad Khan Ahmad Khan, Adnan Adnan; Bashir Bashir, Sajid Sajid
• Other Names: Stankovic Stankovic Srdjan Srdjan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-08-11
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/636858
• Languages: English
• ISSNs: 1687-6172

Abstract

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.