126824 20240731103344.0 doi 10.1007/s00779-023-01721-4 sideral 134276 ART-2023-134276 eng Ospitia-Medina, Yesid ENSA dataset: a dataset of songs by non-superstar artists tested with an emotional analysis based on time-series 2023 Access copy available to the general public Unrestricted This paper presents a novel dataset of songs by non-superstar artists in which a set of musical data is collected, identifying for each song its musical structure, and the emotional perception of the artist through a categorical emotional labeling process. The generation of this preliminary dataset is motivated by the existence of biases that have been detected in the analysis of the most used datasets in the field of emotion-based music recommendation. This new dataset contains 234 min of audio and 60 complete and labeled songs. In addition, an emotional analysis is carried out based on the representation of dynamic emotional perception through a time-series approach, in which the similarity values generated by the dynamic time warping (DTW) algorithm are analyzed and then used to implement a clustering process with the K-means algorithm. In the same way, clustering is also implemented with a Uniform Manifold Approximation and Projection (UMAP) technique, which is a manifold learning and dimension reduction algorithm. The algorithm HDBSCAN is applied for determining the optimal number of clusters. The results obtained from the different clustering strategies are compared and, in a preliminary analysis, a significant consistency is found between them. With the findings and experimental results obtained, a discussion is presented highlighting the importance of working with complete songs, preferably with a well-defined musical structure, considering the emotional variation that characterizes a song during the listening experience, in which the intensity of the emotion usually changes between verse, bridge, and chorus. info:eu-repo/grantAgreement/ES/DGA/T60-23R info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/RTI2018-096986-B-C31 info:eu-repo/grantAgreement/EUR/MINECO/TED2021-130374B-C22 info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 0.654 2023 Library and Information Sciences 2023 Q1 Management Science and Operations Research 2023 Q2 Computer Science Applications 2023 Q2 Hardware and Architecture 2023 Q2 6.6 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Beltrán, José Ramón Universidad de Zaragoza (orcid)0000-0002-7500-4650 Baldassarri, Sandra Universidad de Zaragoza (orcid)0000-0002-9315-6391 5008 785 Universidad de Zaragoza Dpto. Ingeniería Electrón.Com. Área Tecnología Electrónica 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 27 (2023), 1909-192 Personal and Ubiquitous Computing Personal and Ubiquitous Computing 1617-4909 1352676 http://zaguan.unizar.es/record/126824/files/texto_completo.pdf Versión publicada 2344969 http://zaguan.unizar.es/record/126824/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:126824 articulos driver 2024-07-31-09:51:22 ARTICLE