# Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features

arXiv: https://arxiv.org/abs/1808.10351 · HTML: https://mlsanity.com/paper/1808.10351/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Leveraging Whisper Embeddings for Audio-based Lyrics Matching](https://mlsanity.com/paper/2510.08176/) (2025)
- [Innovations in Cover Song Detection: A Lyrics-Based Approach](https://mlsanity.com/paper/2406.04384/) (2024)
- [LyCon: Lyrics Reconstruction from the Bag-of-Words Using Large Language Models](https://mlsanity.com/paper/2408.14750/) (2024)
- [Machine Learning Framework for Audio-Based Content Evaluation using MFCC, Chroma, Spectral Contrast, and Temporal Feature Engineering](https://mlsanity.com/paper/2411.00195/) (2024)
- [Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings](https://mlsanity.com/paper/2601.11262/) (2026)

## Abstract (arXiv)

Cover song detection is a very relevant task in Music Information Retrieval (MIR) studies and has been mainly addressed using audio-based systems. Despite its potential impact in industrial contexts, low performances and lack of scalability have prevented such systems from being adopted in practice for large applications. In this work, we investigate whether textual music information (such as metadata and lyrics) can be used along with audio for large-scale cover identification problem in a wide digital music library. We benchmark this problem using standard text and state of the art audio similarity measures. Our studies shows that these methods can significantly increase the accuracy and scalability of cover detection systems on Million Song Dataset (MSD) and Second Hand Song (SHS) datasets. By only leveraging standard tf-idf based text similarity measures on song titles and lyrics, we achieved 35.5% of absolute increase in mean average precision compared to the current scalable audio content-based state of the art methods on MSD. These experimental results suggests that new methodologies can be encouraged among researchers to leverage and identify more sophisticated NLP-based techniques to improve current cover song identification systems in digital music libraries with metadata.