Browsing by Author "Oramas, Sergio"

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  • A deep multimodal approach for cold-start music recommendation
    Oramas, Sergio; Nieto Caballero, Oriol; Sordo, Mohamed; Serra, Xavier (ACM Association for Computer Machinery, 2017)
    An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In ...
  • A rule-based approach to extracting relations from music tidbits
    Oramas, Sergio; Sordo, Mohamed; Espinosa-Anke, Luis (ACM Association for Computer Machinery, 2015)
    This paper presents a rule based approach to extracting relations from unstructured music text sources. The proposed approach identifies and disambiguates musical entities in text, such as songs, bands, persons, albums and ...
  • A semantic hybrid approach for sound recommendation
    Ostuni, Vito Claudio; Di Noia, Tommaso; Di Sciascio, Eugenio; Oramas, Sergio; Serra, Xavier (ACM Association for Computer Machinery, 2015)
    In this work we describe a hybrid recommendation approach for recommending sounds to users by exploiting and semantically enriching textual information such as tags and sounds descriptions. As a case study we used Freesound, ...
  • A semantic-based approach for artist similarity
    Oramas, Sergio; Sordo, Mohamed; Espinosa-Anke, Luis; Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2015)
    This paper describes and evaluates a method for computing artist similarity from a set of artist biographies. The/nproposed method aims at leveraging semantic information present in these biographies, and can be divided ...
  • Automatic creation of knowledge graphs from digital musical document libraries
    Oramas, Sergio; Sordo, Mohamed; Serra, Xavier (Society for Interdisciplinary Musicology, 2014)
    Most of the current musicological knowledge is present in printed books and manuscripts. In the last years greats efforts have been done in order to digitize and make available these documents in form of Digital Libraries. ...
  • ELMD: an automatically generated entity linking gold standard dataset in the music domain
    Oramas, Sergio; Espinosa-Anke, Luis; Sordo, Mohamed; Saggion, Horacio; Serra, Xavier (European Language Resources Association, 2016)
    In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain. It contains thousands of musical named entities such as Artist, Song or Record Label, which have been automatically annotated ...
  • ELMDist: a vector space model with words and MusicBrainz entities
    Espinosa-Anke, Luis; Oramas, Sergio; Saggion, Horacio; Serra, Xavier (Springer, 2017)
    Music consumption habits as well as the Music market have changed dramatically due to the increasing popularity of digital audio and streaming services. Today, users are closer than ever to a vast number of songs, albums, ...
  • Exploring customer reviews for music genre classification and evolutionary studies
    Oramas, Sergio; Espinosa-Anke, Luis; Lawlor, Aonghus; Serra, Xavier; Saggion, Horacio (International Society for Music Information Retrieval (ISMIR), 2016)
    In this paper, we explore a large multimodal dataset of about 65k albums constructed from a combination of Amazon customer reviews, MusicBrainz metadata and AcousticBrainz audio descriptors. Review texts are further enriched ...
  • Extending tagging ontologies with domain specific knowledge
    Font Corbera, Frederic; Oramas, Sergio; Fazekas, György; Serra, Xavier (CEUR Workshop Proceedings, 2014)
    Currently proposed tagging ontologies are mostly focused on the definition of a common schema for representing the agents involved in a tagging process. In this paper we describe preliminary research around the idea of ...
  • Extracting relations from unstructured text sources for music recommendation
    Sordo, Mohamed; Oramas, Sergio; Espinosa-Anke, Luis (Springer, 2015)
    This paper presents a method for the generation of struc- tured data sources for music recommendation using information extracted from unstructured text sources. The proposed method identi es entities in text that are ...
  • Finding and expanding hypernymic relations in the music domain
    Espinosa-Anke, Luis; Oramas, Sergio; Camacho-Collados, Jose; Saggion, Horacio (2016)
    Lexical taxonomies are tree or directed acyclic graph-like structures where each node represents a concept and each edge encodes a binary hypernymic (is-a) relation. These lexical resources are useful for AI tasks like ...
  • FLABASE: A Flamenco Knowledge Base
    Oramas, Sergio (2018-03-16)
    FlaBase (Flamenco Knowledge Base) is the acronym of a new knowledge base of flamenco music. Its ultimate aim is to gather all available online editorial, biographical and musicological information related to flamenco music. ...
  • FlaBase: Towards the creation of a flamenco music knowledge base
    Oramas, Sergio; Gómez, Francisco; Gómez Gutiérrez, Emilia, 1975-; Mora, Joaquín (International Society for Music Information Retrieval (ISMIR), 2015)
    Online information about flamenco music is scattered over different sites and knowledge bases. Unfortunately, there is no common repository that indexes all these data. In this work, information related to flamenco ...
  • 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 ...
  • Information extraction for knowledge base construction in the music domain
    Oramas, Sergio; Espinosa-Anke, Luis; Sordo, Mohamed; Saggion, Horacio; Serra, Xavier (Elsevier, 2016)
    The rate at which information about music is being created and shared on the web is growing exponentially. However, the challenge of making sense of all this data remains an open problem. In this paper, we present and ...
  • KBSF (Information Extraction for Knowledge Base Construction in the Music Domain)
    Oramas, Sergio; Espinosa-Anke, Luis (Universitat Pompeu Fabra, 2016-01)
    Knowledge Base automatically extracted from songfacts.com following the methodology described in the following paper: Sergio Oramas, Luis Espinosa-Anke, Mohamed Sordo, Horacio Saggion, Xavier Serra, Information extraction ...
  • Knowledge is out there: a new step in the evolution of music digital libraries
    Oramas, Sergio; Sordo, Mohamed (A-R Editions, 2016)
    Musicological knowledge is spread between the lines of thousands of texts stored in hundreds of music libraries. Technology, and more recently semantic technologies, may play a key role in the way information is retrieved. ...
  • MARD: Multimodal Album Reviews Dataset
    Oramas, Sergio (2016)
    MARD contains texts and accompanying metadata originally obtained from a much larger dataset of Amazon customer reviews, which have been enriched with music metadata from MusicBrainz, and audio descriptors from AcousticBrainz. ...
  • 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, ...
  • MediaEval 2018 AcousticBrainz genre task: A baseline combining deep feature embeddings across datasets
    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 ...

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