47 citations · 171 across the 16 of their papers we have counts for
16 papers
Contrastive Learning for Cross-modal Artist Retrieval
Andres Ferraro, Jaehun Kim, Sergio Oramas +2
Music retrieval and recommendation applications often rely on content features encoded as embeddings, which provide vector representations of items in a music dataset. Numerous com…
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship
Andres Ferraro, Gustavo Ferreira, Fernando Diaz +1
Recommender systems have become the dominant means of curating cultural content, significantly influencing individual cultural experience. Since recommender systems tend to optimiz…
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship
Andres Ferraro, Gustavo Ferreira, Fernando Diaz +1
Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience. While the majority of resea…
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…
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…
What is fair? Exploring the artists' perspective on the fairness of music streaming platforms
Andres Ferraro, Xavier Serra, Christine Bauer
Music streaming platforms are currently among the main sources of music consumption, and the embedded recommender systems significantly influence what the users consume. There is a…
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…
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…
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…
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…
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…
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…
On large-scale genre classification in symbolically encoded music by automatic identification of repeating patterns
Andres Ferraro, Kjell Lemström
The importance of repetitions in music is well-known. In this paper, we study music repetitions in the context of effective and efficient automatic genre classification in large-sc…
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…
Using offline metrics and user behavior analysis to combine multiple systems for music recommendation
Andres Ferraro, Dmitry Bogdanov, Kyumin Choi +1
There are many offline metrics that can be used as a reference for evaluation and optimization of the performance of recommender systems. Hybrid recommendation approaches are commo…
Automatic playlist continuation using a hybrid recommender system combining features from text and audio
Andres Ferraro, Dmitry Bogdanov, Jisang Yoon +2
The ACM RecSys Challenge 2018 focuses on music recommendation in the context of automatic playlist continuation. In this paper, we describe our approach to the problem and the fina…