• DocumentCode
    821463
  • Title

    Modeling and classification of natural sounds by product code hidden Markov models

  • Author

    Woodard, Jeffrey P.

  • Author_Institution
    Autonetics, Anaheim, CA, USA
  • Volume
    40
  • Issue
    7
  • fYear
    1992
  • fDate
    7/1/1992 12:00:00 AM
  • Firstpage
    1833
  • Lastpage
    1835
  • Abstract
    Linear predictive coding (LPC), vector quantization (VQ), and hidden Markov models (HMMs) are three popular techniques from speech recognition which are applied in modeling and classifying nonspeech natural sounds. A new structure called the product code HMM uses two independent HMM per class, one for spectral shape and one for gain. Classification decisions are made by scoring shape and gain index sequences from a product code VQ. In a series of classification experiments, the product code structure outperformed the conventional structure, with an accuracy of over 96% for three classes
  • Keywords
    Markov processes; acoustic signal processing; codes; filtering and prediction theory; pattern recognition; LPC; VQ; acoustic signal processing; classification experiments; gain index sequences; linear predictive coding; nonspeech natural sound classification; pattern recognition; product code HMM; product code hidden Markov models; spectral shape; vector quantization; Acoustic distortion; Acoustic measurements; Distortion measurement; Gain measurement; Hidden Markov models; Linear predictive coding; Product codes; Psychoacoustic models; Spectral shape; Speech recognition;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.143457
  • Filename
    143457