Browsing by Author "Ferraro, Andrés"

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  • A computational exploration of melodic patterns in Arab-Andalusian music
    Nuttall, Thomas; Casado, Miguel G.; Ferraro, Andrés; Conklin, Darrell; Caro Repetto, Rafael (Taylor & Francis, 2021)
    Here we present a computational approach to identifying melodic patterns in a dataset of 145 MusicXML scores with the aim of contributing to centonization theory in the Moroccan tradition of Arab-Andalusian Music – a theory ...
  • A tool for the analysis and discovery of Ottoman-Turkish Makam music
    Sentürk, Sertan; Ferraro, Andrés; Porter, Alastair; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2016)
    We present a web application for the analysis and discovery of Ottoman-Turkish makam music. The tool uses an audio-score alignment methodology developed for this music culture for the analysis. It stores the data to be ...
  • Artist and style exposure bias in collaborative filtering based music recommendations
    Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier; Yoon, Jason (ACM FAT Network, 2019)
    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 different styles. In this on-going work ...
  • Artist biases in collaborative filtering for music recommendation
    Ferraro, Andrés; Jeon, Jea Ho; Kim, Biho; Serra, Xavier; Bogdanov, Dmitry (ICML, 2020)
    To evaluate if the recommendations are fair, we have to consider how all the stakeholders are affected. In this work, we focus on the artists in the music domain. We analyze the recommendations made with Collaborative ...
  • Automatic playlist continuation using a hybrid recommender system combining features from text and audio
    Ferraro, Andrés; Bogdanov, Dmitry; Yoon, Jisang; Kim, KwangSeob; Serra, Xavier (ACM Association for Computer Machinery, 2018)
    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 final hybrid system that was submitted to the ...
  • Break the loop: gender imbalance in music recommenders
    Ferraro, Andrés; Serra, Xavier; Bauer, Christine (ACM Association for Computer Machinery, 2021)
    As recommender systems play an important role in everyday life, there is an increasing pressure that such systems are fair. Besides serving diverse groups of users, recommenders need to represent and serve item providers ...
  • Enriched music representations with multiple cross-modal contrastive learning
    Ferraro, Andrés; Favory, Xavier; Drossos, Konstantinos; Kim, Yuntae; Bogdanov, Dmitry (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    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 obtain representations using various sources ...
  • Exploring longitudinal effects of session-based recommendations
    Ferraro, Andrés; Jannach, Dietmar; Serra, Xavier (ACM Association for Computer Machinery, 2020)
    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 ongoing session. The lack of long-term ...
  • Freesound datasets: a platform for the creation of open audio datasets
    Fonseca, Eduardo; Pons Puig, Jordi; Favory, Xavier; Font Corbera, Frederic; Bogdanov, Dmitry; Ferraro, Andrés; Oramas, Sergio; Porter, Alastair; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2017)
    Openly available datasets are a key factor in the advancement of data-driven research approaches, including many of the ones used in sound and music computing. In the last few years, quite a number of new audio datasets ...
  • How low can you go? Reducing frequency and time resolution in current CNN architectures for music auto-tagging
    Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier; Jeon, Jay Ho; Yoon, Jason (Institute of Electrical and Electronics Engineers (IEEE), 2020)
    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 advances in deep learning. In particular, many ...
  • Maximizing the engagement: exploring new signals of implicit feedback in music recommendations
    Ferraro, Andrés; Oramas, Sergio; Quadrana, Massimo; Serra, Xavier (CEUR Workshop Proceedings, 2020)
    Music recommendation engines play a pivotal role in connecting artists with their listeners. Optimizing myopically only for user satisfaction may lead systems to recommend just a small fraction of all the available artists, ...
  • MEL: a music entity linking system
    Oramas, Sergio; Ferraro, Andrés; Correya, Albin Andrew; Serra, Xavier (2017)
    In this work, we present MEL, the first Music Entity Linking system. MEL is able to identify mentions of musical entities (e.g., album, songs, and artists) in free text, and disambiguate them to a music knowledge base, ...
  • Melon playlist dataset: a public dataset for audio-based playlist generation and music tagging
    Ferraro, Andrés; Kim, Yuntae; Lee, Soohyeon; Kim, Biho; Jo, Namjun; Lim, Semi; Lim, Suyon; Jang, Jungtaek; Kim, Sehwan; Serra, Xavier; Bogdanov, Dmitry (Institute of Electrical and Electronics Engineers (IEEE), 2021)
    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 of copyrighted commercial music. We present ...
  • Music recommender systems: taking into account the artists' perspective
    Ferraro, Andrés (Universitat Pompeu Fabra, 2021-09-30)
    Music streaming platforms nowadays play an important role in music consumption and have a big influence on the musical taste of the listeners. Machine learning-based recommender systems are a fundamental part of such ...
  • On large-scale genre classification in symbolically encoded music by automatic identification of repeating patterns
    Ferraro, Andrés; Lemström, Kjell (ACM Association for Computer Machinery, 2018)
    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-scale music-databases. We aim at enhancing ...
  • Skip prediction using boosting trees based on acoustic features of tracks in sessions
    Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier (ACM Association for Computer Machinery, 2019)
    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 problem and the final system that was ...
  • Using offline metrics and user behavior analysis to combine multiple systems for music recommendation
    Ferraro, Andrés; Bogdanov, Dmitry; Choi, Kyumin; Serra, Xavier (2018)
    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 commonly used to improve some of those metrics ...
  • What is fair? Exploring the artists’ perspective on the fairness of music streaming platforms
    Ferraro, Andrés; Serra, Xavier; Bauer, Christine (Springer, 2021)
    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 an increasing interest to ensure that those ...