Add to ORKGThis 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. 1
(A) Values of the GLMMs computed separately for each animal species. For each species included in ou...
Bibliography: leaves 203-218.xii, 218, [4] leaves : ill. (some col.) ; 30 cm.This thesis investigate...
Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to u...
This paper introduces the Greenwood function cepstral coefficient (GFCC) and generalized perceptual ...
A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to cal...
Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysi...
Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysi...
In this paper a comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequ...
Many animals emit vocal sounds which, independently from the sounds' function, contain some individu...
Animals produce vocalizations that range in complexity from a single repeated call to hundreds of un...
Many animals emit vocal sounds which, independently from the sounds' function, embed some individual...
2 Summary Identification of vocal categories (e.g. call types, individuals, species) is a key task w...
* Birdsong often contains large amounts of rapid frequency modulation (FM). It is believed that the ...
Summary. A major goal of evolutionary biology is to understand the dynamics of natural selection wit...
(A) Values of the GLMMs computed across animal species. We assessed acoustic predictors of humans’ a...
(A) Values of the GLMMs computed separately for each animal species. For each species included in ou...
Bibliography: leaves 203-218.xii, 218, [4] leaves : ill. (some col.) ; 30 cm.This thesis investigate...
Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to u...
This paper introduces the Greenwood function cepstral coefficient (GFCC) and generalized perceptual ...
A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to cal...
Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysi...
Bioacoustics, the study of animal vocalizations, has begun to use increasingly sophisticated analysi...
In this paper a comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequ...
Many animals emit vocal sounds which, independently from the sounds' function, contain some individu...
Animals produce vocalizations that range in complexity from a single repeated call to hundreds of un...
Many animals emit vocal sounds which, independently from the sounds' function, embed some individual...
2 Summary Identification of vocal categories (e.g. call types, individuals, species) is a key task w...
* Birdsong often contains large amounts of rapid frequency modulation (FM). It is believed that the ...
Summary. A major goal of evolutionary biology is to understand the dynamics of natural selection wit...
(A) Values of the GLMMs computed across animal species. We assessed acoustic predictors of humans’ a...
(A) Values of the GLMMs computed separately for each animal species. For each species included in ou...
Bibliography: leaves 203-218.xii, 218, [4] leaves : ill. (some col.) ; 30 cm.This thesis investigate...
Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to u...