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Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e.g., &#x2018;A&#x2019;, &#x2018;B&#x2019;, and &#x2018;C&#x2019;). However, explicitly identifying the function of each segment (e.g., &#x2018;verse&#x2019; or &#x2018;chorus&#x2019;) 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.

To Catch A Chorus, Verse, Intro, or Anything Else: Analyzing a Song with Structural Functions