23 citations · 27 across the 5 of their papers we have counts for
6 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…
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…
Pre-Training Strategies Using Contrastive Learning and Playlist Information for Music Classification and Similarity
Pablo Alonso-Jiménez, Xavier Favory, Hadrien Foroughmand +4
In this work, we investigate an approach that relies on contrastive learning and music metadata as a weak source of supervision to train music representation models. Recent studies…
Multilabel Prototype Generation for Data Reduction in k-Nearest Neighbour classification
Jose J. Valero-Mas, Antonio Javier Gallego, Pablo Alonso-Jiménez +1
Prototype Generation (PG) methods are typically considered for improving the efficiency of the -Nearest Neighbour (NN) classifier when tackling high-size corpora. Such approa…
TensorFlow Audio Models in Essentia
Pablo Alonso-Jiménez, Dmitry Bogdanov, Jordi Pons +1
Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predicti…