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
Link To Document