文献类型：期刊文献

英文题名：Two-layer similarity fusion model for cover song identification

作者：Chen, Ning[1];Li, Mingyu[1];Xiao, Haidong[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China;[2]Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai, Peoples R China

年份：2017

卷号：2017

期号：1

外文期刊名：EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING

收录：;EI(收录号：20172203718754);WOS:【SCI-EXPANDED(收录号:WOS:000404161800001)】；

基金：This work was supported by the National Natural Science Foundation of China [grant number 61271349].

语种：英文

外文关键词：Cover Song Identification (CSI); Music Information Retrieval (MIR); Early fusion; Late fusion; Two-layer similarity fusion

摘要：Various musical descriptors have been developed for Cover Song Identification (CSI). However, different descriptors are based on various assumptions, designed for representing distinct characteristics of music, and often differ in scale and noise level. Therefore, a single similarity function combined with a specific descriptor is generally not able to describe the similarity between songs comprehensively and reliably. In this paper, we propose a two-layer similarity fusion model for CSI, which combines the information carried by different descriptors and similarity functions organically and incorporates the advantages of both early fusion and late fusion. In particular, in the early fusion, the similarities obtained by the same descriptor and different similarity functions are integrated with the Similarity Network Fusion (SNF) technique. Then, in the late fusion, the learning method selected by sparse group LASSO algorithm is applied on each early fused similarity to obtain the probability that the corresponding song pair belongs to the reference/cover pair. Lastly, the final fused similarity is achieved by averaging all the obtained probabilities. Extensive experimental results on the music collection that is composed of samples provided by the SecondHandSongs (SHS) verify that the proposed scheme outperforms state-of-the-art fusion based CSI schemes in terms of identification accuracy and classification efficiency.