most citedEvaluation of CNN-based Automatic Music Tagging Models

47 citations · 79 across the 6 of their papers we have counts for

collaborators

8 papers

eess.AS20202 cited

The Freesound Loop Dataset and Annotation Tool

Antonio Ramires, Frederic Font, Dmitry Bogdanov +7

Music loops are essential ingredients in electronic music production, and there is a high demand for pre-recorded loops in a variety of styles. Several commercial and community dat…

eess.AS202047 cited

Evaluation of CNN-based Automatic Music Tagging Models

Minz Won, Andres Ferraro, Dmitry Bogdanov +1

Recent advances in deep learning accelerated the development of content-based automatic music tagging systems. Music information retrieval (MIR) researchers proposed various archit…

eess.AS2020

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…

cs.IR20198 cited

Artist and style exposure bias in collaborative filtering based music recommendations

Andres Ferraro, Dmitry Bogdanov, Xavier Serra +1

Algorithms have an increasing influence on the music that we consume and understanding their behavior is fundamental to make sure they give a fair exposure to all artists across di…

cs.IR2019

How Low Can You Go? Reducing Frequency and Time Resolution in Current CNN Architectures for Music Auto-tagging

Andres Ferraro, Dmitry Bogdanov, Xavier Serra +2

Automatic tagging of music is an important research topic in Music Information Retrieval and audio analysis algorithms proposed for this task have achieved improvements with advanc…

cs.IR20191 cited

Skip prediction using boosting trees based on acoustic features of tracks in sessions

Andrés Ferraro, Dmitry Bogdanov, Xavier Serra

The Spotify Sequential Skip Prediction Challenge focuses on predicting if a track in a session will be skipped by the user or not. In this paper, we describe our approach to this p…