论文库

Automatic setlist identification aims to identify the songs performed in live concert recordings by matching noisy and variable-quality audio against a database of reference tracks. This task presents challenges beyond conventional music version identification, including substantial arrangement differences, background noise, and the need to process continuous recordings efficiently at large scale. In this work, we evaluate modern music version retrieval methods for setlist identification using the ASID benchmark, comparing the established Re-MOVE system with CLEWS and NAVE, an internal neural audio version embedding model. Our results show that NAVE and CLEWS substantially outperform Re-MOVE. Notably, NAVE uses 256-dimensional embeddings, compared with 1024 dimensions for CLEWS, providing a four-fold reduction in embedding storage while achieving better retrieval performance. We further evaluate music/non-music gating and find modest improvements in detection and temporal coverage, while qualitative analysis suggests that such preprocessing can improve recognition boundaries beyond what the standard ASID metrics capture. Overall, the results demonstrate that segment-level audio embeddings provide a strong basis for scalable setlist identification and that compact representations can achieve strong accuracy under realistic live-concert conditions at much lower storage and serving cost.