000125918 001__ 125918
000125918 005__ 20241125101138.0
000125918 0247_ $$2doi$$a10.9781/ijimai.2022.04.002
000125918 0248_ $$2sideral$$a133436
000125918 037__ $$aART-2023-133436
000125918 041__ $$aeng
000125918 100__ $$0(orcid)0000-0002-6584-7259$$aÁlvarez, P.$$uUniversidad de Zaragoza
000125918 245__ $$aRiada: a machine-learning based infrastructure for recognising the emotions of Spotify songs
000125918 260__ $$c2023
000125918 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125918 5203_ $$aThe music emotions can help to improve the personalization of services and contents offered by music streaming providers. Many research works based on the use of machine learning techniques have addressed the problem of recognising the music emotions during the last years. Nevertheless, the results obtained are only applied on small-size music repositories and do not consider what the users feel when they listen to the songs. These issues prevent the existing proposals to be integrated into the personalization mechanisms of the online music providers. In this paper, we present the RIADA infrastructure which is composed by a set of systemsable to annotate emotionally the catalog of songs offered by Spotify based on the users’ perception. RIADA works with the Spotify playlist miner and data services to build emotion recognition models that can solve the open challenges previously mentioned. Machine learning algorithms, music information retrieval techniques, architectures for parallelization of applications and cloud computing have been combined to develop a complex result of engineering able to integrate the music emotions into the Spotify-based applications.
000125918 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB$$9info:eu-repo/grantAgreement/ES/DGA/T21-20R-DISCO$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096986-B-C31$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R
000125918 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125918 590__ $$a3.4$$b2023
000125918 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b62 / 170 = 0.365$$c2023$$dQ2$$eT2
000125918 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b78 / 197 = 0.396$$c2023$$dQ2$$eT2
000125918 594__ $$a7.2$$b2023
000125918 592__ $$a0.904$$b2023
000125918 593__ $$aStatistics and Probability$$c2023$$dQ1
000125918 593__ $$aComputer Networks and Communications$$c2023$$dQ2
000125918 593__ $$aComputer Vision and Pattern Recognition$$c2023$$dQ2
000125918 593__ $$aSignal Processing$$c2023$$dQ2
000125918 593__ $$aArtificial Intelligence$$c2023$$dQ2
000125918 593__ $$aComputer Science Applications$$c2023$$dQ2
000125918 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125918 700__ $$aGarcía de Quirós, J.$$uUniversidad de Zaragoza
000125918 700__ $$0(orcid)0000-0002-9315-6391$$aBaldassarri, S.$$uUniversidad de Zaragoza
000125918 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000125918 773__ $$g8, 2 (2023), 168-181$$pInt. j. interact. multimed. artif. intell.$$tInternational journal of interactive multimedia and artificial intelligence$$x1989-1660
000125918 8564_ $$s1328578$$uhttps://zaguan.unizar.es/record/125918/files/texto_completo.pdf$$yVersión publicada
000125918 8564_ $$s3369701$$uhttps://zaguan.unizar.es/record/125918/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125918 909CO $$ooai:zaguan.unizar.es:125918$$particulos$$pdriver
000125918 951__ $$a2024-11-22-12:01:34
000125918 980__ $$aARTICLE