Presentation Details
| An Open-Source Computational Tool for Generating Synthetic Vocal Identity Signals Through String Sonification Raneem Samman1, 2, Alexandra G.Juárez1, Grace Smith-Vidaurre1, 2, 3. 1Department of Integrative Biology, Michigan State University, East Lansing, Michigan, USA.2Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, East Lansing, Michigan, USA.3Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, Michigan, USA |
Abstract
Testing ideas about how vocalizations are used to transmit information about social identity, such as group and individual identity, can yield new insights into the evolutionary origins of vocal production learning. However, empirical datasets of animal vocalizations are often noisy and incomplete, and the identity information encoded in these vocalizations is unknown, making it difficult to test theoretical predictions. To address this challenge, we developed an open-access R package, paRsynth, that interfaces with the soundgen package to create synthetic datasets of vocal identity signals, with identity information encoded in frequency modulation patterns. This package facilitates generating synthetic vocal signals through the sonification of character strings that contain group and individual identity information. Our second version of paRsynth allows manipulating the location of identity information and the base encoding used to convert symbols into frequency modulation. This research software development project has produced a valuable tool for future simulation-based and empirical research on the evolution and function of vocal communication that relies on social learning.
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No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
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