Wavelet-based dynamic time warping

  • Authors:

  • Sylvio Barbon, Jr.;Rodrigo Capobianco Guido;Lucimar Sasso Vieira;Everthon Silva Fonseca;Fabrício Lopes Sanchez;Paulo Rogério Scalassara;Carlos Dias Maciel;José Carlos Pereira;Shi-Huang Chen

  • Affiliations:

  • Institute of Physics of São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;Institute of Physics of São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;Institute of Physics of São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;School of Engineering at São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;School of Engineering at São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;School of Engineering at São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;School of Engineering at São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;School of Engineering at São Carlos, University of São Paulo, USP, São Carlos, São Paulo 13566-590, Brazil;Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung County, 824, Taiwan, ROC

  • Venue:
  • Journal of Computational and Applied Mathematics
  • Year:
  • 2009

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Abstract

Dynamic Time Warping (DTW), a pattern matching technique traditionally used for restricted vocabulary speech recognition, is based on a temporal alignment of the input signal with the template models. The principal drawback of DTW is its high computational cost as the lengths of the signals increase. This paper shows extended results over our previously published conference paper, which introduces an optimized version of the DTW that is based on the Discrete Wavelet Transform (DWT).