# A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing
## 3 Experiments
### Experimental Results
#### 3.3.2 Comparison with State-Of-The-Art CSI Schemes  
To verify the efficiency of the proposed scheme in comparison with other CSI schemes that are based on single similarity function or similarity fusion, the MAP, MaRR, and TOP-10 achieved by each scheme are listed in Table 3. The CSI schemes included in this experiment were the proposed model (denoted as TLSFP--two-level similarity fusion and post-processing); HPCP-Qmax [4]; HPCP-Dmax [5]; a particle swarm optimization (PSO)-based scheme [21]; a high space (HS) mapping-based scheme [11]; the scheme proposed in [8] (denoted as SNF-2); the scheme proposed in [12] (denoted as SNF-3); SNF-4, which fuses the similarities based on HPCP-Qmax, HPCP-Dmax, MLD-Qmax, and MLD-Dmax with SNF; and a two-layer fusion based scheme [7]. For the HS and PSO schemes, the same similarity types as those in SNF-4 were adopted.  
The experimental results shown in Table 3 demonstrate that the proposed TLSFP scheme outperformed the other CSI schemes (based on single similarity function or similarity fusion) included in terms of MAP, MaRR, and TOP-10, on all six datasets except for the MAP value on DB3187. The gap was 0.0069, which is very small and can be neglected.  
Figure 5: Identification accuracy achieved in each step of the proposed model on (**first column**) DB160, (**second column**) DB400, and (**last column**) DB3172. BL: baseline; DIFF: diffusion processing; MAP: mean of average precision; MaRR: mean averaged reciprocal rank; TOP-10: total number of covers identified in TOP 10.  
_Appl. Sci._**2018**, \(8\), 1383  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Datasets** & **Algorithm** & **MAP** & **MaRR** & **TOP-10** \\ \hline  & HPCP-Qmax [4] & 0.5435 & 0.2831 & 98 \\  & HPCP-Dmax [5] & 0.5709 & 0.2979 & 104 \\  & PSO (HPCP-Qmax) [21] & 0.5758 & 0.2993 & 101 \\  & HS [11] & 0.5868 & 0.3086 & 107 \\ DB160 & SNF-2 [8] & 0.6247 & 0.3269 & 114 \\  & SNF-3 [12] & 0.6413 & 0.3346 & 113 \\  & SNF-4 & 0.6479 & 0.3369 & 114 \\  & Two-layer-fusion [7] & 0.6680 & 0.6680 & 119 \\  & TLSFP & **0.6817** & **0.6817** & **125** \\ \hline  & HPCP-Qmax [4] & 0.8227 & 0.1907 & 2852 \\  & HPCP-Dmax [5] & 0.7945 & 0.1907 & 2717 \\  & PSO [21] & 0.7933 & 0.2445 & 2571 \\  & HS [11] & 0.7564 & 0.1883 & 2651 \\ DB400 & SNF-2 [8] & 0.9359 & 0.2040 & 3286 \\  & SNF-3 [12] & 0.9611 & 0.2080 & 3408 \\  & SNF-4 & 0.9848 & 0.2118 & 3529 \\  & Two-layer-fusion [7] & 0.9754 & 0.3094 & 3482 \\  & TLSFP & **0.9866** & **0.3107** & **3545** \\ \hline  & HPCP-Qmax [4] & 0.4448 & 0.2831 & 3538 \\  & HPCP-Dmax [5] & 0.4412 & 0.2059 & 3501 \\  & PSO [21] & 0.4593 & 0.2101 & 3634 \\  & HS [11] & 0.3536 & 0.1691 & 2832 \\ DB3172 & SNF-2 [8] & 0.5399 & 0.2379 & 4556 \\  & SNF-3 [12] & 0.5004 & 0.2238 & 3962 \\  & SNF-4 & 0.5602 & 0.2468 & 4602 \\  & Two-layer-fusion [7] & 0.5622 & 0.4579 & 4734 \\  & TLSFP & **0.5673** & **0.4590** & **4787** \\ \hline  & HPCP-Qmax [4] & 0.4296 & 0.1877 & 4647 \\  & HPCP-Dmax [5] & 0.4321 & 0.1921 & 4567 \\  & PSO [21] & 0.4442 & 0.1938 & 4768 \\  & HS [11] & 0.2947 & 0.1366 & 3103 \\ DB3183 & SNF-2 [8] & 0.5512 & 0.2285 & 6015 \\  & SNF-3 [12] & 0.4893 & 0.2064 & 5177 \\  & SNF-4 & 0.5508 & 0.2285 & 6015 \\  & Two-layer-fusion [7] & 0.5546 & 0.4147 & 6309 \\  & TLSFP & **0.5693** & **0.4221** & **6461** \\ \hline \end{tabular}
\end{table}
Table 3: Identification accuracy comparison among different cover song identification (CSI) schemes. HS: high space; PSO: particle swarm optimization; TLSFP: two-level similarity fusion and post-processing.