4 papers
Towards Robust Version Identification in the Wild: A Dataset, Benchmark, and Fine-Tuning Study
Simon Hachmeier, R. Oguz Araz, Dmitry Bogdanov +2
Existing datasets for musical version identification (VI) are primarily derived from curated metadata sources such as SecondHandSongs and Discogs, and are therefore dominated by pr…
HumMusQA: A Human-written Music Understanding QA Benchmark Dataset
Benno Weck, Pablo Puentes, Andrea Poltronieri +2
The evaluation of music understanding in Large Audio-Language Models (LALMs) requires a rigorously defined benchmark that truly tests whether models can perceive and interpret musi…
Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification
R. Oguz Araz, Guillem Cortès-SebastiÃ, Emilio Molina +4
Audio fingerprinting (AFP) allows the identification of unknown audio content by extracting compact representations, termed audio fingerprints, that are designed to remain robust a…
Supervised contrastive learning from weakly-labeled audio segments for musical version matching
Joan SerrÃ, R. Oguz Araz, Dmitry Bogdanov +1
Detecting musical versions (different renditions of the same piece) is a challenging task with important applications. Because of the ground truth nature, existing approaches match…