On the robustness of cover version identification models: a study using cover versions from YouTube

Abstract

Introduction. Recent advances in cover version identification have shown great success. However, models are usually tested on a fixed set of datasets which are relying on the online cover version database SecondHandSongs. It is unclear how well models perform on cover versions on online video platforms, which might exhibit alterations that are not expected.

Method. We annotate a subset of versions from YouTube sampled by a multi-modal uncertainty sampling approach and evaluate state-of-the-art cover version identification models.

Results. We find that existing models achieve significantly lower ranking performance on our dataset compared to a community dataset. We additionally measure the performance of different types of versions (e.g., instrumental versions) and find several types that are particularly hard to rank. Lastly, we provide a taxonomy of alterations in cover versions on the web.

Conclusions. We found that research in cover version identification shall be less dependent on SecondHandSongs but rather on more diverse datasets.