Two-layer similarity fusion model for cover song identification

Abstract

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.

A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing

“…Considering that the similarity between two tracks can be calculated based on different descriptors and similarity functions, the complementary properties are neglected while using a single similarity function. It has been verified [6][7][8] that different descriptors and similarity functions are complementary to each other in the CSI task. To fully take advantage of the common as well as complementary information contained in different descriptors and similarity functions in describing the similarity between tracks, some researchers began to study similarity fusion algorithms for CSI.…”

“…Then, the mutual proximity (MP) technique [15] is performed on the diffused similarity scores to reduce the bad influence caused by the "hubness" phenomenon existing in the diffused track community. It should be noted that the proposed scheme is different from our previously proposed scheme [7] in the following respects: (i) Unlike the scheme in [7], the proposed scheme is fully unsupervised. (ii) The track manifold contained in the two-level fused similarity graph is not considered in [7].…”

“…This achieved the highest identification accuracy in the CSI task of MIREX 2016 (http://www.music-ir.org/mirex/wiki/2016:Audio_Cover_Song_Identification_Results). Some researchers proposed multi-stage similarity fusion schemes to take advantage of the common and complementary information provided by different musical descriptors and different similarity functions at the same time [7,14]. In [14], the SNF technique was applied to both the descriptor-level fusion and the similarity-level fusion.…”

“…It achieved the highest identification accuracy on the Covers80 dataset. In [7], in the early fusion, the similarities obtained by the same descriptor and different similarity functions were integrated by SNF. In the late fusion, the learning method selected by the sparse group LASSO algorithm was applied to the early fused similarity to obtain the probability that the input track pair belonged to the reference/cover pair.…”

“…However, some important factors that may seriously influence the identification accuracy are not considered in the available fusion schemes: (i) The complementarity among different descriptors and that among different similarity functions is not considered simultaneously [6,8] or not fused efficiently [7]. (ii) The track manifold of the fused similarity graph, which will affect retrieval accuracy greatly, is not taken into consideration [15] (refer to Section 2.4.1 for specific examples).…”