LPC spectral moments for clustering acoustic transients

Author

Pinkowski, Ben

Author_Institution

Dept. of Comput. Sci., Western Michigan Univ., Kalamazoo, MI, USA

Volume

1

Issue

3

fYear

1993

fDate

7/1/1993 12:00:00 AM

Firstpage

362

Lastpage

368

Abstract

Spectral moments (mean and coefficients of variation, skewness, and kurtosis) are assessed for 40 samples from 10 groups of acoustic transient signals differing in harmonic structure, duration, and degree of spectral overlap. Discriminant analysis involving moments based on linear predictive coding (LPC) resulted in a higher recognition rate for pulsed-tone sounds (87%) that were more like human speech than for pure-tone sounds (70%). By contrast, classification based on moments calculated from the discrete Fourier transform (DFT) yielded 85% recognition for both groups. Cluster analyses indicated that LPC-based moments were more characteristic of relationships among the 10 sound groups and especially the two tonal groups, though results were somewhat dependent on LPC model order

Keywords

linear predictive coding; speech coding; speech recognition; transients; DFT; LPC model order; acoustic transient signals; classification; cluster analysis; discrete Fourier transform; discriminant analysis; kurtosis; linear discriminant function; linear predictive coding; pulsed-tone sounds; pure-tone sounds; skewness; spectral moments; speech recognition; Acoustic distortion; Discrete Fourier transforms; Distortion measurement; Frequency; Humans; Linear predictive coding; Signal processing; Speech analysis; Speech processing; Speech recognition;

fLanguage

English

Journal_Title

Speech and Audio Processing, IEEE Transactions on

Publisher

ieee

ISSN

1063-6676

Type

jour

DOI

10.1109/89.232619

Filename

232619