activity
20192022
most citedEvaluation of CNN-based Automatic Music Tagging Models

47 citations · 150 across the 11 of their papers we have counts for

collaborators

12 papers

cs.IR2022

Offline Retrieval Evaluation Without Evaluation Metrics

Fernando Diaz, Andres Ferraro

Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or no…

cs.SD20214 cited

Improving Sound Event Classification by Increasing Shift Invariance in Convolutional Neural Networks

Eduardo Fonseca, Andres Ferraro, Xavier Serra

Recent studies have put into question the commonly assumed shift invariance property of convolutional networks, showing that small shifts in the input can affect the output predict…

cs.SD202123 cited

Enriched Music Representations with Multiple Cross-modal Contrastive Learning

Andres Ferraro, Xavier Favory, Konstantinos Drossos +2

Modeling various aspects that make a music piece unique is a challenging task, requiring the combination of multiple sources of information. Deep learning is commonly used to obtai…

cs.SD20211 cited

Melon Playlist Dataset: a public dataset for audio-based playlist generation and music tagging

Andres Ferraro, Yuntae Kim, Soohyeon Lee +8

One of the main limitations in the field of audio signal processing is the lack of large public datasets with audio representations and high-quality annotations due to restrictions…

cs.IR202028 cited

Exploring Longitudinal Effects of Session-based Recommendations

Andres Ferraro, Dietmar Jannach, Xavier Serra

Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an o…

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…