Speech enhancement using state-based estimation and sinusoidal modeling

Michael E. Deisher, Andreas Spanias

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

A procedure for estimating the parameters of a sinusoidal model from speech corrupted by additive noise is described. An approximate harmonic representation is used wherein voiced speech is represented by a set of sine waves at multiples of the fundamental frequency and several additional components at frequencies near each harmonic. Amplitudes and phases of the sinusoidal components are estimated using a state-based technique that employs hidden Markov models (HMMs) to classify speech and noise spectra. Voicing and fundamental frequency are determined using an analysis-by- synthesis approach. Simulation results are presented, comparing the performance of the proposed algorithm to that of the standard HMM-based minimum mean square error (MMSE) estimator. The proposed method was found to reduce the structured residual noise associated with HMM-based algorithms.

Original languageEnglish (US)
Pages (from-to)1141-1149
Number of pages9
JournalJournal of the Acoustical Society of America
Volume102
Issue number2 pt 1
DOIs
StatePublished - Aug 1 1997

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Acoustics and Ultrasonics

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