# Learning a Representation for Cover Song Identification Using Convolutional Neural Network

arXiv: https://arxiv.org/abs/1911.00334 · HTML: https://mlsanity.com/paper/1911.00334/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 8 of 8)

- [Contrastive Learning with Positive-Negative Frame Mask for Music Representation](https://mlsanity.com/paper/2203.09129/) (2022)
- [CoverHunter: Cover Song Identification with Refined Attention and Alignments](https://mlsanity.com/paper/2306.09025/) (2023)
- [And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?](https://mlsanity.com/paper/2210.01256/) (2022)
- [Leveraging Whisper Embeddings for Audio-based Lyrics Matching](https://mlsanity.com/paper/2510.08176/) (2025)
- [Real-world Music Plagiarism Detection With Music Segment Transcription System](https://mlsanity.com/paper/2509.08282/) (2025)
- [A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-By-Humming Task](https://mlsanity.com/paper/2312.01092/) (2023)
- [An approach to hummed-tune and song sequences matching](https://mlsanity.com/paper/2410.20352/) (2024)
- [Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings](https://mlsanity.com/paper/2601.11262/) (2026)

## Abstract (arXiv)

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.