3 citations · 3 across the 6 of their papers we have counts for
7 papers
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…
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…
Benchmarking Music Autotagging with MGPHot Expert Annotations vs. Generic Tag Datasets
Pedro Ramoneda, Pablo Alonso-Jiménez, Sergio Oramas +2
Music autotagging aims to automatically assign descriptive tags, such as genre, mood, or instrumentation, to audio recordings. Due to its challenges, diversity of semantic descript…
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…
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…
Efficient Supervised Training of Audio Transformers for Music Representation Learning
Pablo Alonso-Jiménez, Xavier Serra, Dmitry Bogdanov
In this work, we address music representation learning using convolution-free transformers. We build on top of existing spectrogram-based audio transformers such as AST and train o…