Riada: a machine-learning based infrastructure for recognising the emotions of Spotify songs

Álvarez, P. (Universidad de Zaragoza) ; García de Quirós, J. (Universidad de Zaragoza) ; Baldassarri, S. (Universidad de Zaragoza)

Resumen: 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.

Idioma: Inglés

DOI: 10.9781/ijimai.2022.04.002

Año: 2023

Publicado en: International journal of interactive multimedia and artificial intelligence 8, 2 (2023), 168-181
ISSN: 1989-1660

Factor impacto JCR: 3.4 (2023)

Categ. JCR: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS rank: 62 / 170 = 0.365 (2023) - Q2 - T2
Categ. JCR: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE rank: 78 / 197 = 0.396 (2023) - Q2 - T2

Factor impacto CITESCORE: 7.2 - Computer Science Applications (Q1) - Statistics and Probability (Q1) - Signal Processing (Q1) - Computer Networks and Communications (Q1) - Computer Vision and Pattern Recognition (Q1) - Artificial Intelligence (Q2)

Factor impacto SCIMAGO: 0.904 - Statistics and Probability (Q1) - Computer Networks and Communications (Q2) - Computer Vision and Pattern Recognition (Q2) - Signal Processing (Q2) - Artificial Intelligence (Q2) - Computer Science Applications (Q2)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FEDER/T60-20R-AFFECTIVE LAB
Financiación: info:eu-repo/grantAgreement/ES/DGA/T21-20R-DISCO
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096986-B-C31
Financiación: info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84796-C2-2-R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)