A Stochastic Approximation Method for Waveform Cluster Center Generation

William J. Steingrandt, Stephen S. Yau

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

A method is presented that detects behavioral transients in waveforms. 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. Clustering is assumed to be with respect to an arbitrary distance measure. This measure is assumed to satisfy differentiability and regularity requirements. The algorithm is shown to converge based on assumptions concerning a unique optimum. This is done by the application of a stochastic-approximation theorem to a gradient-following technique. The resulting algorithm is applied to a problem in speech processing. The structure resulting from the learning algorithm is compared to the standard linguistic phonetic structure.

Original languageEnglish (US)
Pages (from-to)262-274
Number of pages13
JournalIEEE Transactions on Information Theory
Volume18
Issue number2
DOIs
StatePublished - Mar 1972
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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