Histogram matching for music repetition detection

Aibo Tian, Wen Li, Linxing Xiao, Dong Wang, Jie Zhou, Tong Zhang

Abstract: Repetition detection is a fundamental issue for music thumbnailing and summarization. In this paper, we propose a new feature, called chroma histogram, which enables us to find out repetitive segments from popular songs accurately and quickly. The feature is robust to tempo variation, because sequential information is removed during the process. The low dimensional feature guarantees a very low computational cost, which is proved by theoretic analysis and experimental evaluation. The objective evaluation results demonstrate that our algorithm outperforms previous approaches in terms of both detecting accuracy and efficiency.