  Chorus detection is a challenging problem in musical signal processing as the
chorus often repeats more than once in popular songs, usually with rich
instruments and complex rhythm forms. Most of the existing works focus on the
receptiveness of chorus sections based on some explicit features such as
loudness and occurrence frequency. These pre-assumptions for chorus limit the
generalization capacity of these methods, causing misdetection on other
repeated sections such as verse. To solve the problem, in this paper we propose
an end-to-end chorus detection model DeepChorus, reducing the engineering
effort and the need for prior knowledge. The proposed model includes two main
structures: i) a Multi-Scale Network to derive preliminary representations of
chorus segments, and ii) a Self-Attention Convolution Network to further
process the features into probability curves representing chorus presence. To
obtain the final results, we apply an adaptive threshold to binarize the
original curve. The experimental results show that DeepChorus outperforms
existing state-of-the-art methods in most cases.
