Browsing by Author "Ferraro, Andrés"

Sort by: Order: Results:

  • 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 ...
  • 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 ...
  • Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier; Yoon, Jason; Comunicació presentada a: The 1st Workshop on Designing Human-Centric MIR Systems, esdeveniment satèl·lit d'ISMIR 2019, celebrat a Delft, Holanda, el dia 2 de novembre de 2019. (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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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, ...
  • 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, ...
  • 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 ...
  • Ferraro, Andrés; Bogdanov, Dmitry; Serra, Xavier (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 ...
  • 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 ...