Audio-based Musical Version Identification: Elements and Challenges
This is an incremental review paper that synthesizes existing literature to help researchers and practitioners in audio-based music retrieval build better systems.
The paper reviews 20 years of research on musical version identification, highlighting the historical accuracy-scalability trade-off and noting that recent deep learning approaches have improved both aspects, making deployment in industrial applications more feasible.
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.