Browsing by Author "Bogdanov, Dmitry"

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  • 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 ...
  • Ramires, António; Font Corbera, Frederic; Bogdanov, Dmitry; Smith, Jordan B. L.; Yang, Yi-Hsuan; Ching, Joann; Chen, Bo-Yu; Wu, Yueh-Kao; Wei-Han, Hsu; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2020)
    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 databases have been created to meet this ...
  • Bogdanov, Dmitry; Serrà Julià, Joan; Wack, Nicolas; Herrera Boyer, Perfecto, 1964- (Institute of Electrical and Electronics Engineers (IEEE), 2009)
    Studying the ways to recommend music to a user is a central task within the music information research community. From a content-based point of view, this task can be regarded as obtaining a suitable distance measurement ...
  • Bogdanov, Dmitry (Universitat Pompeu Fabra, 2013-10-10)
    Aquest treball es centra en el modelatge d'usuari per la recomanació musical i desenvolupa algoritmes per la comprensió automàtica i visualització de preferències musicals. Primer, es proposa un model d'usuari construït a ...
  • 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 ...
  • Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964- (International Society for Music Information Retrieval (ISMIR), 2011)
    In this work we consider distance-based approaches to music recommendation, relying on an explicit set of music tracks provided by the user as evidence of his/her music preferences. Firstly, we propose a purely content-based ...
  • Alonso Jiménez, Pablo; Pepino, Leonardo; Batlle-Roca, Roser; Zinemanas, Pablo; Bogdanov, Dmitry; Serra, Xavier; Rocamora, Martín (Institute of Electrical and Electronics Engineers (IEEE), 2024)
    We present PECMAE an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, ...
  • Oramas, Sergio; Bogdanov, Dmitry; Porter, Alastair (CEUR Workshop Proceedings, 2018)
    In this paper we present a baseline approach for the MediaEval 2018 AcousticBrainz Genre Task that takes advantage of stacking multiple feature embeddings learned on individual genre datasets by simple deep learning ...
  • Bogdanov, Dmitry; Porter, Alastair; Tovstogan, Philip; Won, Minz (CEUR Workshop Proceedings, 2019)
    This paper provides an overview of the Emotion and Theme recognition in Music task organized as part of the MediaEval 2019 Benchmarking Initiative for Multimedia Evaluation. The goal of this task is to automatically ...
  • 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 ...
  • Porter, Alastair; Bogdanov, Dmitry; Serra, Xavier (ACM Association for Computer Machinery, 2016)
    Semantic annotations of music collections in digital libraries are important for organization and navigation of the collection. These annotations and their associated metadata are useful in many Music Information Retrieval ...
  • Bogdanov, Dmitry; Lizarraga Seijas, Xavier; Alonso-Jiménez, Pablo; Serra, Xavier (2022-09-27)
    We present MusAV, a new public benchmark dataset for comparative validation of arousal and valence (AV) regression models for audio-based music emotion recognition. To gather the ground truth, we rely on relative ...
  • Bogdanov, Dmitry; Lizarraga Seijas, Xavier; Alonso-Jiménez, Pablo; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2022)
    We present MusAV, a new public benchmark dataset for comparative validation of arousal and valence (AV) regression models for audio-based music emotion recognition. To gather the ground truth, we rely on relative judgments ...
  • Alonso-Jiménez, Pablo; Serra, Xavier; Bogdanov, Dmitry (2022-09-22)
    This paper revisits the idea of music representation learning supervised by editorial metadata, contributing to the state of the art in two ways. First, we exploit the public editorial metadata available on Discogs, an ...
  • Alonso-Jiménez, Pablo; Serra, Xavier; Bogdanov, Dmitry (International Society for Music Information Retrieval (ISMIR), 2022)
    This paper revisits the idea of music representation learning supervised by editorial metadata, contributing to the state of the art in two ways. First, we exploit the public editorial metadata available on Discogs, an ...
  • Yang, Yi-Hsuan; Bogdanov, Dmitry; Herrera Boyer, Perfecto, 1964-; Sordo, Mohamed (ACM Association for Computer Machinery, 2012)
    The emergence of social tagging websites such as Last.fm has provided new opportunities for learning computational models that automatically tag music. Researchers typically obtain music tags from the Internet and use them ...
  • Haro Berois, Martín; Xambó, Anna; Fuhrmann, Ferdinand; Bogdanov, Dmitry; Gómez Gutiérrez, Emilia, 1975-; Herrera Boyer, Perfecto, 1964- (ACM Association for Computer Machinery, 2010)
    The music we like (i.e. our musical preferences) encodes and communicates key information about ourselves. Depicting such preferences in a condensed and easily understandable way is very appealing, especially considering ...
  • Alonso-Jiménez, Pablo; Favory, Xavier; Foroughmand, Hadrien; Bourdalas, Grigoris; Serra, Xavier; Lidy, Thomas; Bogdanov, Dmitry (2023-04-25)
    In this work, we investigate an approach that relies on contrastive learning and music metadata as a weak source of supervision to train music representation models. Recent studies show that contrastive learning can be ...
  • Bogdanov, Dmitry; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2017)
    While a vast amount of editorial metadata is being actively gathered and used by music collectors and enthusiasts, it is often neglected by music information retrieval and musicology researchers. In this paper we propose ...
  • Tovstogan, Philip; Serra, Xavier; Bogdanov, Dmitry (Sound and Music Computing, 2022)
    Music recommendation systems are commonly used for personalized recommendations. However, there are cases where due to privacy concerns or design decisions, there is no user information nor collaborative filtering data ...

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