Investigating the efficacy of music version retrieval systems for setlist identification
This research addresses the challenging and under-explored problem of automatic setlist identification for the music industry, providing a method to automatically catalog live music performances.
This paper tackles the problem of setlist identification (SLI) in live music events, aiming to retrieve metadata and timestamps for all played tracks. The proposed end-to-end workflow, utilizing a version identification system, successfully identifies 68% of annotated segments, with performance varying from 35% to 77% depending on the genre. Even with a large reference set of 56.8k songs, the system maintains a 56% identification rate.
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.