Papers › Learning a Representation for Cover Song Identification Using Convolutional Neural Network

Learning a Representation for Cover Song Identification Using Convolutional Neural Network

1 Nov 2019arXiv 2019 11arXiv:1911.00334archive 2025-07-28

Zhesong Yu, Xiaoshuo Xu, Xiaoou Chen, Deshun Yang

Cover song identification represents 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 employing neural network approaches. In this paper, we propose a novel Convolutional Neural Network (CNN) architecture based on the characteristics of the cover song task. We first train the network through classification strategies; the network is then used to extract music representation for cover song identification. A scheme is designed to train robust models against tempo changes. Experimental results show that our approach outperforms state-of-the-art methods on all public datasets, improving the performance especially on the large dataset.

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