  Conventional music structure analysis algorithms aim to divide a song into
segments and to group them with abstract labels (e.g., 'A', 'B', and 'C').
However, explicitly identifying the function of each segment (e.g., 'verse' or
'chorus') is rarely attempted, but has many applications. We introduce a
multi-task deep learning framework to model these structural semantic labels
directly from audio by estimating "verseness," "chorusness," and so forth, as a
function of time. We propose a 7-class taxonomy (i.e., intro, verse, chorus,
bridge, outro, instrumental, and silence) and provide rules to consolidate
annotations from four disparate datasets. We also propose to use a
spectral-temporal Transformer-based model, called SpecTNT, which can be trained
with an additional connectionist temporal localization (CTL) loss. In
cross-dataset evaluations using four public datasets, we demonstrate the
effectiveness of the SpecTNT model and CTL loss, and obtain strong results
overall: the proposed system outperforms state-of-the-art chorus-detection and
boundary-detection methods at detecting choruses and boundaries, respectively.
