Stochastic approximation method for waveform cluster center generation

WJ STEINGRANDT WJ, Sik-Sang Yau

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

A structure is defined that accepts waveforms as inputs and generates a sequence of symbols representing the sequence of transients present in the waveform. This structure is developed by generalizing an unsupervised learning algorithm to the time- varying case. The algorithm accepts a sequence of unlabeled waveforms to find cluster centers associated with the transients. The algorithm is shown to converge based on assumptions concerning a unique optimum. This is done by the application of stochastic- approximation theorem to a gradient- following technique. The resulting algorithm is applied to a problem in speech processing.

Original languageEnglish (US)
Pages (from-to)262-274
Number of pages13
JournalIEEE Transactions on Information Theory
VolumeIT-18
Issue number2
Publication statusPublished - Mar 1972
Externally publishedYes

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ASJC Scopus subject areas

  • Information Systems
  • Electrical and Electronic Engineering

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