Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

1.    Passive acoustic monitoring (PAM) has the
potential to greatly improve our ability to monitor cryptic yet vocal
animals. Advances in automated signal detection have increased the scope
of PAM, but distinguishing between individuals— which is necessary for
density estimation— remains a major challenge. When individual identity is
known, supervised classification techniques can be used to distinguish
between individuals. Supervised methods require labeled training data,
whereas unsupervised techniques do not. If the acoustic signals of
individuals are sufficiently different, the number of clusters might
represent the number of individuals sampled. The majority of applications
of unsupervised techniques in animal vocalizations have focused on
quantifying species-specific call repertoires. However, with increased
interest in PAM applications, unsupervised methods that can distinguish
between individuals are needed.  2.  
 Here, we use an existing dataset of Bornean gibbon female calls
with known identity from five sites on Malaysian Borneo to test the
ability of three different unsupervised clustering algorithms (affinity
propagation, K-medoids, and Gaussian mixture model-based clustering) to
distinguish between individuals. Calls from different gibbon females are
readily distinguishable using supervised techniques. For internal
validation of unsupervised cluster solutions, we calculated silhouette
coefficients. For external validation, we compared clustering results with
female identity labels using a standard metric: normalized mutual
information. We also calculated classification accuracy by assigning
unsupervised cluster solutions to females based on which cluster had the
highest number of calls from a particular female. 3.  
 We found that affinity propagation clustering consistently
outperformed the other algorithms for all metrics used. In particular,
classification accuracy of affinity propagation clustering was more
consistent as the number of females increased, and when we randomly
sampled females across sites.  4.  
 We conclude that unsupervised techniques may be useful for
providing additional information regarding individual identity for PAM
applications. We stress that although we use gibbons as a case study,
these methods will be applicable for any individually-distinct vocal
animal.