63 citations · 63 across the 7 of their papers we have counts for
9 papers
Training Music Sample Identification Models on Real Sample Pairs
R. Oguz Araz, Joan Serrà, Xavier Lizarraga-Seijas +4
Sample identification (SI) is the task of matching pairs of tracks, where one track is created by musically transforming an element of the other. In the absence of sample annotatio…
Building a Dataset for Music Sample Identification
R. Oguz Araz, Xavier Lizarraga, Xavier Serra +1
Sample identification (SI) is the task of matching an element of a musical work to its musically transformed versions used to create new works. The task has received little attenti…
Unified Music Identification for Tracks and Versions
R. Oguz Araz, Joan Serrà, Yuki Mitsufuji +2
Given a music database, track identification (TI) retrieves the exact track matching an audio excerpt, whereas version identification (VI) retrieves its musical versions. Tradition…
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…
OMAR-RQ: Open Music Audio Representation Model Trained with Multi-Feature Masked Token Prediction
Pablo Alonso-Jiménez, Pedro Ramoneda, R. Oguz Araz +2
Developing open-source foundation models is essential for advancing research in music audio understanding and ensuring access to powerful, multipurpose representations for music in…
Enhancing Neural Audio Fingerprint Robustness to Audio Degradation for Music Identification
R. Oguz Araz, Guillem Cortès-Sebastià, 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 Serrà, 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…
Discogs-VI: A Musical Version Identification Dataset Based on Public Editorial Metadata
R. Oguz Araz, Xavier Serra, Dmitry Bogdanov
Current version identification (VI) datasets often lack sufficient size and musical diversity to train robust neural networks (NNs). Additionally, their non-representative clique s…
Universal Speech Enhancement with Score-based Diffusion
Joan Serrà, Santiago Pascual, Jordi Pons +2
Removing background noise from speech audio has been the subject of considerable effort, especially in recent years due to the rise of virtual communication and amateur recordings.…