Learning a Representation for Cover Song Identification Using Convolutional Neural Network

Abstract

Cover song identification is a challenging task in the field of Music Information Retrieval (MIR) due to complex musical variations between query tracks and cover versions. Previous works typically utilize hand-crafted features and alignment algorithms for the task. More recently, further breakthroughs are achieved by employing neural network approaches. In this paper, we propose a novel Convolutional Neural Network (CNN) towards cover song identification. We train the network through classification criteria. Having been trained, the network is used to extract music representation for cover song identification. A training scheme is designed to train robust models against tempo changes. Experimental results show that our approach outperforms state-of-the-art methods on several public datasets with low time complexity.