2026-04-13T05:05:15Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/521842025-12-22T13:41:53Zcom_2072_6col_2072_452952

Personalized musically induced emotions of not-so-popular Colombian music Gómez Cañón, Juan Sebastián Herrera Boyer, Perfecto, 1964- Cano, Estefanía Gómez Gutiérrez, Emilia, 1975- Comunicació presentada al workshop Human Centered AI inclòs a: 35th Conference on Neural Information Processing Systems (NeurIPS 2021) celebrat el 13 de desembre de manera virtual. This work presents an initial proof of concept of how Music Emotion Recognition (MER) systems could be intentionally biased with respect to annotations of musically-induced emotions in a political context. In specific, we analyze traditional Colombian music containing politically-charged lyrics of two types: (1) vallenatos and social songs from the “left-wing” guerrilla Fuerzas Armadas Revolucionarias de Colombia (FARC) and (2) corridos from the “right-wing” paramilitaries Autodefensas Unidas de Colombia (AUC). We train personalized machine learning models to predict induced emotions for three users with diverse political views – we aim at identifying the songs that may induce negative emotions for a particular user, such as anger and fear. To this extent, a user’s emotion judgements could be interpreted as problematizing data – subjective emotional judgments could in turn be used to influence the user in a human-centered machine learning environment. In short, highly desired “emotion regulation” applications could potentially deviate to “emotion manipulation” – the recent discredit of emotion recognition technologies might transcend ethical issues of diversity and inclusion. The research work conducted at the Universitat Pompeu Fabra is partially supported by the Eu- ropean Commission under the TROMPA project (H2020 770376) and the Project Musical AI - PID2019-111403GB-I00/AEI/10.13039/501100011033 funded by the Spanish Ministerio de Ciencia, Innovación y Universidades (MCIU) and the Agencia Estatal de Investigación (AEI). 2022-01-11T10:37:03Z 2022-01-11T10:37:03Z 2021 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion info:eu-repo/grantAgreement/EC/H2020/770376 info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00 © The Authors. This paper is licensed under a Creative Commons License (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)) https://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess NeurIPS