  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?
