Current state of bioacoustics research of vocal repertoires
- 1Max-Planck-Institute for Neurobiology of Behaviour - caesar
Abstract
Knowledge of the unique call types produced by a species has historically been an important part of ecological research. However, the definition of a call type is not straightforward, and across species and studies different methods are employed to study call types and vocal repertories. Here, we analysed the methodology used to identify the vocal repertoires of 197 mammalian species across 220 studies. We found a large variability in the features extract from individual calls, methods to analyse spectrographic features, and methods to cluster call types. We further see a low uptake of novel methods for acoustic analysis and clustering. Lastly, we identify a lack of open data across the included studies, with less than 2% of studies providing the raw audio dataset underlying their paper. Based on these results, we call for increased standardization of methods and data transparency across the field.
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BibTeX
@misc{grijseels2026current,
title = {{Current state of bioacoustics research of vocal repertoires}},
author = {D. M. Grijseels and P. Malkemper},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.756971},
url = {https://www.biorxiv.org/content/10.64898/2026.10.06.756971v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Current state of bioacoustics research of vocal repertoires
AU - D. M. Grijseels
AU - P. Malkemper
PY - 2026
DA - 2026/10/06
PB - bioRxiv
DO - 10.64898/2026.10.06.756971
UR - https://www.biorxiv.org/content/10.64898/2026.10.06.756971v1
AB - Knowledge of the unique call types produced by a species has historically been an important part of ecological research. However, the definition of a call type is not straightforward, and across species and studies different methods are employed to study call types and vocal repertories. Here, we analysed the methodology used to identify the vocal repertoires of 197 mammalian species across 220 studies. We found a large variability in the features extract from individual calls, methods to analyse spectrographic features, and methods to cluster call types. We further see a low uptake of novel methods for acoustic analysis and clustering. Lastly, we identify a lack of open data across the included studies, with less than 2% of studies providing the raw audio dataset underlying their paper. Based on these results, we call for increased standardization of methods and data transparency across the field.
ER -