Pablo Alonso-Jiménez — accepted AI-conference papers

2023

Pre-Training Strategies Using Contrastive Learning and Playlist Information for Music Classification and Similarity

Pablo Alonso-Jiménez, Xavier Favory, Hadrien Foroughmand, Grigoris Bourdalas, Xavier Serra, Thomas Lidy +1

ICASSP 2023accepted

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 show that contrastive learning can be used with editorial metadata (e.g., artist or album name) to learn audio represen…

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2022

Ambiguity Modelling with Label Distribution Learning for Music Classification

Morgan Buisson, Pablo Alonso-Jiménez, Dmitry Bogdanov

ICASSP 2022accepted

An important amount of work has been devoted to the task of music classification. Despite promising results achieved by convolutional neural networks, there still exists a gap left to be filled for such models to perform well in real-world applications. In this work, we address the issue of ambiguit…

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2020

Tensorflow Audio Models in Essentia

Pablo Alonso-Jiménez, Dmitry Bogdanov, Jordi Pons, Xavier Serra

ICASSP 2020accepted

Essentia is a reference open-source C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">++</sub> /Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trai…

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