Two-layer large-scale cover song identification system based on music structure segmentation

Abstract

This paper focuses on cover song identification over a large-scale dataset. Identifying all covers of a query song from music collection is a challenging task since covers vary in multiple aspects, such as tempo, key, and structure. For the large-scale dataset, cover song identification is more challenging and few works have been published. Previous works usually use a single representation for a whole song, such as 2D Fourier transform and chord profiles, which cannot reflect the property that covers are largely determined by a local similarity. To address this problem, we propose a novel cover song identification method based on music structure segmentation. The proposed structural method identifies cover songs on section level instead of song level. The experimental results show that the structural method improves the mean average precision of 2D Fourier transform method from 9.5% to 12.1%. In addition, we also propose a two-layer cover song identification system to improve the efficiency.