The diversity of qualitative approaches and analytical methods has often undermined comparative research on primate vocal repertoires. The purpose of the present work is to introduce a quantitative method based on dynamic time warping to the study of repertoire size in Eulemur spp. We obtained a large sample of calls of E. coronatus, E. flavifrons, E. fulvus, E. macaco, E. mongoz, E. rubriventer, and E. rufus, recorded between 1999 and 2013 from captive and wild lemurs. We inspected recordings visually using spectrograms, then cut and saved high-quality vocal emissions to single files for further analysis. We extracted the acoustic features of all vocalizations of a species using the Hidden Markov Model Toolkit, an application of dynamic time warping, and then compared cepstral coefficients (a feature widely used in automatic speaker recognition) pairwise. We analyzed the results using affinity propagation clustering. We found that Eulemur species share most of their vocal repertoire but species-specific calls determine repertoire size differences. Repertoire size varied from 9 to 14 vocalization types among species, with a mean of 11.

Comparative Analysis of the Vocal Repertoire of Eulemur: A Dynamic Time Warping Approach

GAMBA, Marco
First
;
FRIARD, Olivier Pierre;RIONDATO, ISIDORO;RIGHINI, ROBERTA;COLOMBO, CAMILLA MARTA PEDINA;TORTI, VALERIA;NADHUROU, BAKRI;GIACOMA, Cristina
2015

Abstract

The diversity of qualitative approaches and analytical methods has often undermined comparative research on primate vocal repertoires. The purpose of the present work is to introduce a quantitative method based on dynamic time warping to the study of repertoire size in Eulemur spp. We obtained a large sample of calls of E. coronatus, E. flavifrons, E. fulvus, E. macaco, E. mongoz, E. rubriventer, and E. rufus, recorded between 1999 and 2013 from captive and wild lemurs. We inspected recordings visually using spectrograms, then cut and saved high-quality vocal emissions to single files for further analysis. We extracted the acoustic features of all vocalizations of a species using the Hidden Markov Model Toolkit, an application of dynamic time warping, and then compared cepstral coefficients (a feature widely used in automatic speaker recognition) pairwise. We analyzed the results using affinity propagation clustering. We found that Eulemur species share most of their vocal repertoire but species-specific calls determine repertoire size differences. Repertoire size varied from 9 to 14 vocalization types among species, with a mean of 11.
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Gamba, Marco; Friard, Olivier; Riondato, Isidoro; Righini, Roberta; Colombo, Camilla; Miaretsoa, Longondraza; Torti, Valeria; Nadhurou, Bakri; Giacoma, Cristina
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1529037
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