# A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing
## 1 Introduction
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). (iii) The bad influence caused by the \"hubness\" phenomenon contained in the fused similarity graph is seldom considered, which may increase the false positive rate [16]. To solve the possible shortcomings existing in the available similarity fusion algorithms and enhance the CSI performances further, a new CSI scheme based on two-level similarity fusion and post-processing is put forward in this paper. At the fusion level, a nonlinear graph fusion technique [13] is first adopted to fuse the similarity graphs constructed based on the same descriptor and different similarity functions. Then, a mixture Markov model (MMM) [17] is introduced to integrate the first-level fused similarity graphs generated based on two complementary descriptors. At the post-processing level, diffusion [16] is first applied on the obtained two-level fused similarity graph to take full advantage of the underlying structure of the tracks contained within it to reduce the noise and enhance the identification further. 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]. (iii) The negative influence of the \"hubness\" phenomenon, which is not considered in [7], is eliminated by the MP technique in the proposed scheme. Extensive experiments conducted on three cover song datasets (Covers80 ([[https://labrosa.ee.columbia.edu/projects/coversongs/covers80/](https://labrosa.ee.columbia.edu/projects/coversongs/covers80/)]([https://labrosa.ee.columbia.edu/projects/coversongs/covers80/](https://labrosa.ee.columbia.edu/projects/coversongs/covers80/))), Covers40, and SHS [[https://labrosa.ee.columbia.edu/millionsong/secondhand](https://labrosa.ee.columbia.edu/millionsong/secondhand)]([https://labrosa.ee.columbia.edu/millionsong/secondhand](https://labrosa.ee.columbia.edu/millionsong/secondhand))) manifest the necessity and effectiveness of each step included in the proposed model (Section 3.3.1) and the superiority of the proposed scheme, in terms of CSI identification accuracy over state-of-the-art CSI schemes (Section 3.3.2) and computational complexity, especially when the size of the dataset increased (Section 3.3.3). The rest of this paper is organized as follows. The proposed model is presented in Section 2. Section 3 reports the experimental results. Finally, conclusions are drawn and future work is discussed in Section 4.