Generalized Perceptual Features for Vocalization Analysis Across Multiple Species

Author

Clemins, Patrick J. ; Trawicki, Marek B. ; Adi, Kuntoro ; Tao, Jidong ; Johnson, Michael T.

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI

Volume

1

fYear

2006

fDate

14-19 May 2006

Abstract

This paper introduces the Greenwood function cepstral coefficient (GFCC) and generalized perceptual linear prediction (GPLP) feature extraction models for the analysis of animal vocalizations across arbitrary species. These features are generalizations of the well-known mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) approaches, tailored to take optimal advantage of available knowledge of each species´ auditory frequency range and/or audiogram data. Illustrative results are presented comparing use of the GFCC and GPLP features versus MFCC features over the same frequency ranges

Keywords

bioacoustics; cepstral analysis; feature extraction; speech processing; Greenwood function cepstral coefficient; animal vocalizations; audiogram data; auditory frequency; generalized perceptual linear prediction; mel-frequency cepstral coefficient; multiple species; vocalization analysis; Auditory system; Cepstral analysis; Equations; Feature extraction; Filters; Humans; Mel frequency cepstral coefficient; Power system modeling; Speech processing; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660005

Filename

1660005