Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-task Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2103.14253,
title = {Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-task Learning},
author = {Ju-Chiang Wang and Jordan B. L. Smith and Jitong Chen and Xuchen Song and Yuxuan Wang},
journal= {arXiv preprint arXiv:2103.14253},
year = {2021}
}
Comments
This version is a preprint of an accepted paper by ICASSP2021. Please cite the publication in the Proceedings of IEEE International Conference on Acoustics, Speech, & Signal Processing