Bird sound classification is the task of relating any sound recording to those species of bird that can be heard in the recording. Here, we study bird sound clustering, the task of deciding for any pair of sound recordings whether the same species of bird can be heard in both. We address this problem by first learning, from a training set, probabilities of pairs of recordings being related in this way, and then inferring a maximally probable partition of a test set by correlation clustering. We address the following questions: How accurate is this clustering, compared to a classification of the test set? How do the clusters thus inferred relate to the clusters obtained by classification? How accurate is this clustering when applied to recordings of bird species not heard during training? How effective is this clustering in separating, from bird sounds, environmental noise not heard during training?

翻译：鸟类声音分类是将任意声音记录与记录中可听到的鸟类物种关联起来的任务。本文研究鸟类声音聚类，即判断任意一对声音记录中是否可听到相同鸟类物种的任务。我们通过以下方法解决该问题：首先从训练集中学习成对记录以此方式关联的概率，然后通过相关性聚类推断测试集的最大可能划分。本研究探讨以下问题：与测试集的分类相比，该聚类的准确性如何？通过聚类推断出的簇与通过分类得到的簇之间有何关联？当应用于训练期间未听到的鸟类物种的录音时，该聚类的准确性如何？该聚类在从鸟类声音中分离训练期间未听到的环境噪声方面的有效性如何？