# Audio-based Musical Version Identification: Elements and Challenges

arXiv: https://arxiv.org/abs/2109.02472 · HTML: https://mlsanity.com/paper/2109.02472/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2021.

## Built on by (most cited first, 13 of 13)

- [LLark: A Multimodal Instruction-Following Language Model for Music](https://mlsanity.com/paper/2310.07160/) (2023)
- [A Benchmark and Robustness Study of In-Context-Learning with Large Language Models in Music Entity Detection](https://mlsanity.com/paper/2412.11851/) (2024)
- [And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?](https://mlsanity.com/paper/2210.01256/) (2022)
- [MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection](https://mlsanity.com/paper/2505.20979/) (2025)
- [Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems](https://mlsanity.com/paper/2510.08062/) (2025)
- [DeepResonance: Enhancing Multimodal Music Understanding via Music-centric Multi-way Instruction Tuning](https://mlsanity.com/paper/2502.12623/) (2025)
- [Leveraging Whisper Embeddings for Audio-based Lyrics Matching](https://mlsanity.com/paper/2510.08176/) (2025)
- [Automatic Music Sample Identification with Multi-Track Contrastive Learning](https://mlsanity.com/paper/2510.11507/) (2025)
- [Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges](https://mlsanity.com/paper/2511.07205/) (2025)
- [Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings](https://mlsanity.com/paper/2601.11262/) (2026)
- [Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting](https://mlsanity.com/paper/2604.00927/) (2026)
- [On the Human and Computer Alignment of Attribute-Based Music Matches](https://mlsanity.com/paper/2609.00987/) (2026)
- [JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings](https://mlsanity.com/paper/2610.15356/) (2026)

## Abstract (arXiv)

In this article, we aim to provide a review of the key ideas and approaches proposed in 20 years of scientific literature around musical version identification (VI) research and connect them to current practice. For more than a decade, VI systems suffered from the accuracy-scalability trade-off, with attempts to increase accuracy that typically resulted in cumbersome, non-scalable systems. Recent years, however, have witnessed the rise of deep learning-based approaches that take a step toward bridging the accuracy-scalability gap, yielding systems that can realistically be deployed in industrial applications. Although this trend positively influences the number of researchers and institutions working on VI, it may also result in obscuring the literature before the deep learning era. To appreciate two decades of novel ideas in VI research and to facilitate building better systems, we now review some of the successful concepts and applications proposed in the literature and study their evolution throughout the years.