Format of Original
Institute of Electrical and Electronics Engineers (IEEE)
2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (Volume:1 )
Original Item ID
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
Clemins, Patrick J., "Generalized Perceptual Features for Vocalization Analysis across Multiple Species" (2006). Electrical and Computer Engineering Faculty Research and Publications. 145.