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Cover song identification represents a challenging task in the field of MusicInformation Retrieval (MIR) due to complex musical variations between querytracks and cover versions. Previous works typically utilize hand-craftedfeatures and alignment algorithms for the task. More recently, furtherbreakthroughs are achieved employing neural network approaches. In this paper,we propose a novel Convolutional Neural Network (CNN) architecture based on thecharacteristics of the cover song task. We first train the network throughclassification strategies; the network is then used to extract musicrepresentation for cover song identification. A scheme is designed to trainrobust models against tempo changes. Experimental results show that ourapproach outperforms state-of-the-art methods on all public datasets, improvingthe performance especially on the large dataset.

Learning a Representation for Cover Song Identification Using Convolutional Neural Network