  We introduce a novel low level feature for identifying cover songs which
quantifies the relative changes in the smoothed frequency spectrum of a song.
Our key insight is that a sliding window representation of a chunk of audio can
be viewed as a time-ordered point cloud in high dimensions. For corresponding
chunks of audio between different versions of the same song, these point clouds
are approximately rotated, translated, and scaled copies of each other. If we
treat MFCC embeddings as point clouds and cast the problem as a relative shape
sequence, we are able to correctly identify 42/80 cover songs in the "Covers
80" dataset. By contrast, all other work to date on cover songs exclusively
relies on matching note sequences from Chroma derived features.
