3 citations · 4 across the 3 of their papers we have counts for
4 papers
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…
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…
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…