  This paper presents a novel supervised approach to detecting the chorus
segments in popular music. Traditional approaches to this task are mostly
unsupervised, with pipelines designed to target some quality that is assumed to
define "chorusness," which usually means seeking the loudest or most frequently
repeated sections. We propose to use a convolutional neural network with a
multi-task learning objective, which simultaneously fits two temporal
activation curves: one indicating "chorusness" as a function of time, and the
other the location of the boundaries. We also propose a post-processing method
that jointly takes into account the chorus and boundary predictions to produce
binary output. In experiments using three datasets, we compare our system to a
set of public implementations of other segmentation and chorus-detection
algorithms, and find our approach performs significantly better.
