125918 20241125101138.0 doi 10.9781/ijimai.2022.04.002 sideral 133436 ART-2023-133436 eng Álvarez, P. Universidad de Zaragoza (orcid)0000-0002-6584-7259 Riada: a machine-learning based infrastructure for recognising the emotions of Spotify songs 2023 Access copy available to the general public Unrestricted The 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. info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB info:eu-repo/grantAgreement/ES/DGA/T21-20R-DISCO info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096986-B-C31 info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.4 2023 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 62 / 170 = 0.365 2023 Q2 T2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 78 / 197 = 0.396 2023 Q2 T2 0.904 2023 Statistics and Probability 2023 Q1 Computer Networks and Communications 2023 Q2 Computer Vision and Pattern Recognition 2023 Q2 Signal Processing 2023 Q2 Artificial Intelligence 2023 Q2 Computer Science Applications 2023 Q2 7.2 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion García de Quirós, J. Universidad de Zaragoza Baldassarri, S. Universidad de Zaragoza (orcid)0000-0002-9315-6391 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 8, 2 (2023), 168-181 Int. j. interact. multimed. artif. intell. International journal of interactive multimedia and artificial intelligence 1989-1660 1328578 http://zaguan.unizar.es/record/125918/files/texto_completo.pdf Versión publicada 3369701 http://zaguan.unizar.es/record/125918/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125918 articulos driver 2024-11-22-12:01:34 ARTICLE