# A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing
## 4 Conclusions and Future Work  
In this paper, we propose a music information retrieval scheme based on two-level similarity fusion and post-processing. It adopts different strategies (SNF and MMM) to combine the merits  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Algorithm** & **MAP** & **MaRR** & **TOP-10** \\ \hline \multirow{8}{*}{DB3187} & HPCP-Qmax [4] & 0.4270 & 0.1909 & 4025 \\  & HPCP-Dmax [5] & 0.4189 & 0.1918 & 3862 \\  & PSO [21] & 0.4398 & 0.1967 & 4128 \\  & HS [11] & 0.3127 & 0.1472 & 2981 \\  & SNF-2 [8] & 0.5325 & 0.2270 & 5198 \\  & SNF-3 [12] & 0.4865 & 0.2119 & 4487 \\  & SNF-4 & **0.5513** & 0.2339 & 5410 \\  & Two-layer-fusion [7] & 0.5358 & 0.4168 & 5421 \\  & TLSFP & 0.5444 & **0.4207** & **5502** \\ \hline \multirow{8}{*}{DB3188} & HPCP-Qmax [4] & 0.4485 & 0.2031 & 3835 \\  & HPCP-Dmax [5] & 0.4502 & 0.2084 & 3815 \\  & PSO [21] & 0.4609 & 0.2098 & 3938 \\  & HS [11] & 0.3630 & 0.1711 & 3173 \\  & SNF-2 [8] & 0.5391 & 0.2361 & 4792 \\  & SNF-3 [12] & 0.4951 & 0.2199 & 4193 \\  & SNF-4 & 0.5429 & 0.2383 & 4755 \\  & Two-layer-fusion [7] & 0.5484 & 0.4456 & 4946 \\  & TLSFP & **0.5571** & **0.4504** & **5029** \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 6: The comparison of average computing time achieved by different similarity fusion schemes on four datasets.  
of different similarity functions and those of different descriptors in two fusion levels. In addition, it introduces diffusion and MP techniques to refine the fused similarity scores to enhance cover version identification accuracy. Extensive experiments on three cover song datasets (including Covers80 and SHS) manifested the effectiveness and efficiency of the proposed model in comparison with state-of-the-art CSI schemes.  
TLSFP can be modified and applied to other important tasks in different fields, such as image classification, visual object tracking, cancer subtypes identification, and drug taxonomy, etc. We leave all these problems for future work.  
M.L. conceived of the study, participated in the design of the work, data collection, data analysis, interpretation, and coordination, and drafted the manuscript. N.C. helped to revise the manuscript.  
This work was supported by the National Natural Science Foundation of China [grant number 61771196].  
The authors declare that they have no competing interests.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BL & BaseLine \\ CSI & Cover Song Identification \\ HPCP & Harmonic Pitch Class Profile \\ HS & High Space \\ K-NN & K Nearest Neighbor \\ LCDP & Locally Constrained Diffusion Process \\ MAP & Mean of Average Precision \\ MaRR & Mean Averaged Reciprocal Rank \\ MMM & Mixture Markov Model \\ MLD & Melody \\ MIR & Music Information Retrieval \\ MIREX & Music Information Retrieval Evaluation eXchange \\ MP & Mutual Proximity \\ PSO & Particle Swarm Optimization \\ SHS & Second Hand Songs \\ SNF & Similarity Network Fusion \\ TLSFP & Two-Level Similarity Fusion and Post-Processing \\ TOP-10 & Total Number of Covers Identified in TOP 10 \\ \end{tabular}