  Pattern discovery algorithms in the music domain aim to find meaningful
components in musical compositions. Over the years, although many algorithms
have been developed for pattern discovery in music data, it remains a
challenging task. To gain more insight into the efficacy of these algorithms,
we introduce three computational methods for examining their output: Pattern
Polling, to combine the patterns; Comparative Classification, to differentiate
the patterns; Synthetic Data, to inject predetermined patterns. In combining
and differentiating the patterns extracted by algorithms, we expose how they
differ from the patterns annotated by humans as well as between algorithms
themselves, with rhythmic features contributing the most to the algorithm-human
and algorithm-algorithm discrepancies. Despite the difficulty in reconciling
and evaluating the divergent patterns extracted from algorithms, we identify
some possibilities for addressing them. In particular, we generate controllable
synthesised data with predetermined patterns planted into random data, thereby
leaving us better able to inspect, compare, validate, and select the
algorithms. We provide a concrete example of synthesising data for
understanding the algorithms and expand our discussion to the potential and
limitations of such an approach.
