Learning a Representation for Cover Song Identification Using Convolutional Neural Network

Code

• github.com/Orfium/bytecover
  pytorch★ 32
• github.com/yzspku/CQTNet
  pytorch★ 0

Abstract

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.

Tasks

Benchmark Results

Reproductions