# A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing
## 1 Introduction  
A huge increase in the number of digital music tracks promotes the development of content-based music information retrieval (MIR) technology. As a part of MIR, cover song identification (CSI, also called cover version identification) has received increasing attention due to its potential real-world applications in copyright protection and the management of online music products. Additionally, the study of CSI techniques helps to understand how the human auditory system measures and models the similarity between music. As one of the most fundamental components of MIR applications, how to measure and model similarity between music items is an important yet challenging research question [1]. Various similarity functions have been proposed in recent years [2; 3; 4; 5]. 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. In [9], the main melody and accompaniment of the music were extracted first. Then, the maximum value of the similarities obtained based on main melody, accompaniment, and mixture signal, separately, was taken as the final similarity. In [6],the standard classification-based fusion strategy [10] was adopted to fuse the similarities of three related yet different descriptors (harmony, melody, and bass line). In [11], the fusion of different similarities was achieved by projecting different similarities in a multi-dimensional space, where the dimensionality of the space was the number of similarities considered. However, this scheme was easily disturbed by bad descriptors because of the diluted signal-to-noise ratio. In [12], the similarity graphs obtained based on different descriptors and corresponding similarity functions were fused by the similarity network fusion (SNF) technique [13]. Then, the track-by-track similarities in the fused similarity graph were adopted for version identification. Due to the merits of the SNF technique, this fusion scheme could reduce the noise existing in each similarity graph and take advantage of the common as well as complementary information across each similarity graph. A similar strategy was adopted in [8] to fuse the similarities obtained based on the same descriptor and different similarity functions (Qmax [4] and Dmax [5]). 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](http://www.music-ir.org/mirex/wiki/2016:Audio_Cover_Song_Identification_Results)]([http://www.music-ir.org/mirex/wiki/2016:Audio_Cover_Song_Identification_Results](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. Finally, the final similarity was obtained by averaging the probability-based similarities obtained based on each descriptor.