Title :
Modeling and classification of natural sounds by product code hidden Markov models
Author :
Woodard, Jeffrey P.
Author_Institution :
Autonetics, Anaheim, CA, USA
fDate :
7/1/1992 12:00:00 AM
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;
Journal_Title :
Signal Processing, IEEE Transactions on