5 papers · 1 filter
AllMusicCaps: Album Reviews as Complementary Supervision for Music CLAP
Pablo Alonso-Jiménez, Xavier Lizarraga-Seijas, Xavier Serra +1
Recent open text-audio contrastive models (CLAPs) are typically trained with LLM-generated captions derived from tag datasets or web search results, which tend to be accurate but e…
What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models
Angelos-Nikolaos Kanatas, Yuexuan Kong, Pablo Alonso-Jiménez +2
Music foundation models are commonly used as frozen audio feature extractors, yet selecting which layer to extract from remains largely heuristic. Current practice defaults to fixe…
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…
Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio
Pablo Alonso-Jiménez, Leonardo Pepino, Roser Batlle-Roca +4
We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoen…
mir_ref: A Representation Evaluation Framework for Music Information Retrieval Tasks
Christos Plachouras, Pablo Alonso-Jiménez, Dmitry Bogdanov
Music Information Retrieval (MIR) research is increasingly leveraging representation learning to obtain more compact, powerful music audio representations for various downstream MI…