Dear MARMAM list,
My coauthors and I are pleased to announce the publication of our latest study
in Bioacoustics, entitled "Unsupervised classification of graded animal signals
using fuzzy clustering" (https://doi.org/10.1080/09524622.2025.2563548). In
this study, we implement an innovative clustering algorithm to quantify the
graded nature of long-finned pilot whale calls.
ABSTRACT
Graded vocal repertoires represent a challenge for bioacoustics. We present an
unsupervised classification procedure designed to take gradation into account,
based on Mel frequency cepstral coefficients and fuzzy clustering. Cepstral
coefficients are well defined for tonal, broadband and pulsed sounds. They
compress information about the distribution of energy across frequencies into a
limited number of variables. The Mel scale mimics the perception of pitch by
mammalian ears. Fuzzy clustering is a soft classification approach. Instead of
assigning samples to a single category, it describes their position relative to
overlapping clusters and can therefore identify stereotyped and graded
vocalisations within a repertoire. We evaluated the performance of this
procedure on a set of long-finned pilot whale (Globicephala melas) calls. Fuzzy
clustering was much less time-consuming than manual classification (days vs.
months), but identified a smaller number of categories (three to six fuzzy
clusters compared to 11 human-defined call types). Some fuzzy clusters were
similar to sets of human-defined call types, but some call types were spread
over several fuzzy clusters. Fuzzy clusters provide new quantitative insight
about the gradation of vocal repertoires. We discuss the results and the need
to investigate the functions of call gradation in future research.
Do not hesitate to contact us if you have any question or would like to discuss
the article!
Kind regards,
Benjamin Benti
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