Gómez Cañón, Juan SebastiánGutiérrez Páez, Nicolás FelipePorcaro, LorenzoPorter, AlastairCano, EstefaníaHerrera Boyer, Perfecto, 1964-Gkiokas, AggelosSantos Rodríguez, PatríciaHernández Leo, DaviniaKarreman, CasperGómez Gutiérrez, Emilia, 1975-2023-02-072023-02-072023Gómez‑Cañón JS, Gutiérrez‑Páez N, Porcaro L, Porter A, Cano E, Herrera‑Boyer P, Gkiokas A, Santos P, Hernández‑Leo D, Karreman C, Gómez E. TROMPA-MER: an open dataset for personalized music emotion recognition. J Intell Inf Syst. 2023;60(2):549-70. DOI: 10.1007/s10844-022-00746-00925-9902http://hdl.handle.net/10230/55633We present a platform and a dataset to help research on Music Emotion Recognition (MER). We developed the Music Enthusiasts platform aiming to improve the gathering and analysis of the so-called “ground truth” needed as input to MER systems. Firstly, our platform involves engaging participants using citizen science strategies and generate music emotion annotations – the platform presents didactic information and musical recommendations as incentivization, and collects data regarding demographics, mood, and language from each participant. Participants annotated each music excerpt with single free-text emotion words (in native language), distinct forced-choice emotion categories, preference, and familiarity. Additionally, participants stated the reasons for each annotation – including those distinctive of emotion perception and emotion induction. Secondly, our dataset was created for personalized MER and contains information from 181 participants, 4721 annotations, and 1161 music excerpts. To showcase the use of the dataset, we present a methodology for personalization of MER models based on active learning. The experiments show evidence that using the judgment of the crowd as prior knowledge for active learning allows for more effective personalization of MER systems for this particular dataset. Our dataset is publicly available and we invite researchers to use it for testing MER systems.application/pdfeng© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1007/s10844-022-00746-0Music emotion recognitionPersonalizationActive learningCitizen scienceinfo:eu-repo/semantics/openAccess