Validity-guided fuzzy clustering evaluation for neural network-based time-frequency reassignment

  • Authors:

  • Imran Shafi;Jamil Ahmad;Syed Ismail Shah;Ataul Aziz Ikram;Adnan Ahmad Khan;Sajid Bashir

  • Affiliations:

  • Information and Computing Department, Iqra University, Islamabad, Pakistan;Information and Computing Department, Iqra University, Islamabad, Pakistan;Information and Computing Department, Iqra University, Islamabad, Pakistan;Information and Computing Department, Iqra University, Islamabad, Pakistan;Electrical Engineering Department, College of Telecommunication Engineering, National University of Sciences and Technology, Islamabad, Pakistan;Computer Engineering Department, Centre for Advanced Studies in Engineering, Islamabad, Pakistan

  • Venue:
  • EURASIP Journal on Advances in Signal Processing - Special issue on time-frequency analysis and its applications to multimedia signals
  • Year:
  • 2010

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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.