  The setlist identification (SLI) task addresses a music recognition use case
where the goal is to retrieve the metadata and timestamps for all the tracks
played in live music events. Due to various musical and non-musical changes in
live performances, developing automatic SLI systems is still a challenging task
that, despite its industrial relevance, has been under-explored in the academic
literature. In this paper, we propose an end-to-end workflow that identifies
relevant metadata and timestamps of live music performances using a version
identification system. We compare 3 of such systems to investigate their
suitability for this particular task. For developing and evaluating SLI
systems, we also contribute a new dataset that contains 99.5h of concerts with
annotated metadata and timestamps, along with the corresponding reference set.
The dataset is categorized by audio qualities and genres to analyze the
performance of SLI systems in different use cases. Our approach can identify
68% of the annotated segments, with values ranging from 35% to 77% based on the
genre. Finally, we evaluate our approach against a database of 56.8k songs to
illustrate the effect of expanding the reference set, where we can still
identify 56% of the annotated segments.
