文献类型：期刊文献

英文题名：A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing

作者：Li, Mingyu[1];Chen, Ning[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2018

卷号：8

期号：8

外文期刊名：APPLIED SCIENCES-BASEL

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000442864900173)】；

基金：This work was supported by the National Natural Science Foundation of China [grant number 61771196].

语种：英文

外文关键词：cover song identification; similarity network fusion; mixture Markov model; mutual proximity

摘要：Similarity measurement plays an important role in various information retrieval tasks. In this paper, a music information retrieval scheme based on two-level similarity fusion and post-processing is proposed. At the similarity fusion level, to take full advantage of the common and complementary properties among different descriptors and different similarity functions, first, the track-by-track similarity graphs generated from the same descriptor but different similarity functions are fused with the similarity network fusion (SNF) technique. Then, the obtained first-level fused similarities based on different descriptors are further fused with the mixture Markov model (MMM) technique. At the post-processing level, diffusion is first performed on the two-level fused similarity graph to utilize the underlying track manifold contained within it. Then, a mutual proximity (MP) algorithm is adopted to refine the diffused similarity scores, which helps to reduce the bad influence caused by the "hubness" phenomenon contained in the scores. The performance of the proposed scheme is tested in the cover song identification (CSI) task on three cover song datasets (Covers80, Covers40, and Second Hand Songs (SHS)). The experimental results demonstrate that the proposed scheme outperforms state-of-the-art CSI schemes based on single similarity or similarity fusion.