DOI: 10.5061/dryad.sbcc2fr4p
title: Unsupervised acoustic classification of individual gibbon females and the implications for passive acoustic monitoring

description: 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.    
description: The dataset is comprised of 933 calls (range: 2- 46 calls per
 female) collected from 66 different individual Bornean gibbon (Hylobates
 funereus) females from five different sites in Malaysian Borneo: Maliau
 Basin Conservation Area, Deramakot Forest Reserve, Imbak Canyon
 Conservation Area, Danum Valley Conservation Area and Kalabakan Forest
 Reserve using a Marantz PMD 660 ﬂash recorder (Marantz, Kawasaki, Kanagawa
 Prefecture, Japan) equipped with a Røde NTG-2 directional condenser
 microphone (Røde Microphones, Sydney, Australia). For each call we
 calculated MFCCs over a standardized number of windows (8) for each call,
 and the size of time windows we used to calculate MFCCs varied depending
 on the total duration of the call. For each of the 8 windows, we
 calculated 12 Mel-filters (or bandpass filters; Davis &
 Mermelstein, 1980)) between 500 and 1500 Hz, which corresponds with the
 frequency range of Bornean gibbon female great calls. The first MFCC for
 each time window corresponds to the amplitude or loudness of the signal;
 this will vary depending on the recording distance to the calling animal
 and is therefore not appropriate for discriminative tasks so we only used
 11 MFCCs for each time window. MFCCs describe the spectral envelope at
 particular points in time, but do not capture temporal variation in the
 signal. Therefore, we also calculated delta-cepstral coefficients which
 provide a measure of change from one frame the next, and provide
 information about the temporal dynamics of the signal. As we estimated 11
 MFCCs for each time window, we also had 11 delta coefficients. We also
 included duration, which resulted in a final feature vector of 177
 parameters describing each call. 
description: R code and data is included as follows:<br> 1. Clink and
 Klinck Part 1 Supervised Classification. R code for supervised
 classification.<br> 2. Clink and Klinck Part 2 Unsupervised
 Bootstrapping. R code for recreating analyses and figures for
 bootstrapping over 100 iterations.<br> 3. Clink and Klinck Part 3
 Unsupervised Clustering by site. R code for recreating analyses and
 figures for site-level comparisons.<br> 4.
 ClinkandKlinck2020GibbonFemaleData.csv. Female identity and MFCC feature
 data needed to recreate analyses.<br>