  Mice vocalize in the ultrasonic range during social interactions. These
vocalizations are used in neuroscience and clinical studies to tap into complex
behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has
been traditionally a manual process, which is prone to errors and human bias,
and is not scalable to large scale analysis. We propose a new method to
automatically create a dictionary of USVs based on a two-step spectral
clustering approach, where we split the set of USVs into inlier and outlier
data sets. This approach is motivated by the known degrading performance of
sparse subspace clustering with outliers. We apply spectral clustering to the
inlier data set and later find the clusters for the outliers. We propose
quantitative and qualitative performance measures to evaluate our method in
this setting, where there is no ground truth. Our approach outperforms two
baselines based on k-means and spectral clustering in all of the proposed
performance measures, showing greater distances between clusters and more
variability between clusters.
