  We describe a novel pipeline to automatically discover hierarchies of
repeated sections in musical audio. The proposed method uses similarity network
fusion (SNF) to combine different frame-level features into clean affinity
matrices, which are then used as input to spectral clustering. While prior
spectral clustering approaches to music structure analysis have pre-processed
affinity matrices with heuristics specifically designed for this task, we show
that the SNF approach directly yields segmentations which agree better with
human annotators, as measured by the ``L-measure'' metric for hierarchical
annotations. Furthermore, the SNF approach immediately supports arbitrarily
many input features, allowing us to simultaneously discover structure encoded
in timbral, harmonic, and rhythmic representations without any changes to the
base algorithm.
