Data from: Characterizing vocal repertoires - hard vs. soft classification approaches

To understand the proximate and ultimate causes that shape acoustic
communication in animals, objective characterizations of the vocal
repertoire of a given species are critical, as they provide the foundation
for comparative analyses among individuals, populations and taxa. Progress
in this field has been hampered by a lack of standard in methodology,
however. One problem is that researchers may settle on different variables
to characterize the calls, which may impact on the classification of
calls. More important, there is no agreement how to best characterize the
overall structure of the repertoire in terms of the amount of gradation
within and between call types. Here, we address these challenges by
examining 912 calls recorded from wild chacma baboons (Papio ursinus). We
extracted 118 acoustic variables from spectrograms, from which we
constructed different sets of acoustic features, containing 9, 38, and 118
variables; as well 19 factors derived from principal component analysis.
We compared and validated the resulting classifications of k-means and
hierarchical clustering. Datasets with a higher number of acoustic
features lead to better clustering results than datasets with only a few
features. The use of factors in the cluster analysis resulted in an
extremely poor resolution of emerging call types. Another important
finding is that none of the applied clustering methods gave strong support
to a specific cluster solution. Instead, the cluster analysis revealed
that within distinct call types, subtypes may exist. Because hard
clustering methods are not well suited to capture such gradation within
call types, we applied a fuzzy clustering algorithm. We found that this
algorithm provides a detailed and quantitative description of the
gradation within and between chacma baboon call types. In conclusion, we
suggest that fuzzy clustering should be used in future studies to analyze
the graded structure of vocal repertoires. Moreover, the use of factor
analyses to reduce the number of acoustic variables should be discouraged.