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 labelled 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.

中文翻译：

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长臂猿女性的无监督声学分类及其对被动声学监测的意义

1. 被动声学监测（PAM）可以极大地提高我们监测隐秘而有声动物的能力。自动信号检测技术的进步扩大了PAM的范围，但是区分个体（这是密度估计所必需的）仍然是一个重大挑战。当已知个人身份时，可以使用监督分类技术来区分个人。有监督的方法需要标记的训练数据，而无监督的技术则不需要。如果个体的声信号足够不同，则簇的数量可能代表采样的个体的数量。在动物发声中，无监督技术的大多数应用都集中在量化特定物种的呼叫库中。但是，随着人们对PAM应用程序的兴趣日益浓厚，
2. 在这里，我们使用来自马来西亚婆罗洲五个地点的已知身份的婆罗洲长臂猿女性电话的现有数据集来测试三种不同的无监督聚类算法（亲和力传播，K medoids和基于高斯混合模型的聚类）区分个体的能力。使用监督技术，可以很容易地区分来自不同长臂猿雌性的电话。为了对无监督群集解决方案进行内部验证，我们计算了轮廓系数。对于外部验证，我们使用标准度量（标准化互信息）将聚类结果与女性身份标签进行了比较。我们还通过基于哪个群集从特定女性那里获得最多呼叫的数量，为女性分配无监督的群集解决方案来计算分类准确性。
3. 我们发现，对于所有使用的指标，亲和力传播聚类始终优于其他算法。特别是，随着雌性数量的增加以及当我们跨站点随机抽取雌性时，亲和力传播聚类的分类准确性更加一致。
4. 我们得出的结论是，无监督技术可能有助于为PAM应用程序提供有关个人身份的其他信息。我们强调，尽管我们使用长臂猿作为案例研究，但这些方法将适用于任何单独的声乐动物。