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Article on Music Emotion Recognition published in IEEE Signal Processing Magazine

Article on Music Emotion Recognition published in IEEE Signal Processing Magazine

The journal article “Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications” is a result of the work done during the PhD of Juan Sebastián Gómez-Cañón in the frame of the TROMPA project

03.11.2021

Imatge inicial

This paper presents a review of the challenges and limitations of Music Emotion Recognition (MER), an interdisciplinary research area addressing the characterization of music in terms of emotion. It analyzes music in order to computationally predict the emotions perceived by or induced to a listener.

The authors propose a new user-centric conceptualization framework for MER, highlighting where future researchers should focus efforts: open data and experimental reproducibility, inherent subjectivity of concepts and annotations, model explainability and interpretability, cultural and contextual relevance, and ethical implications for MER applications.

The work concludes with some key ideas: researchers should go beyond the WEIRDness paradigm (studies, music and researchers from Western, Educated, Industrialized, Rich and Democratic countries), take response diversity of data and annotations as an opportunity to allow for individual differences and dataset diversity, embrace the multiplicity of emotion theories and approaches from music psychology, and have in mind arising ethical issues of data privacy, fairness and dual use of affective technologies.

 

This article has been published in IEEE Signal Processing Magazine in November 2021.

J. S. Gómez-Cañón, E. Cano, T. Eerola, P. Herrera, X. Hu, Y. Yang, E. Gómez, "Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications," in IEEE Signal Processing Magazine, vol. 38, no. 6, pp. 106-114, Nov. 2021, doi: 10.1109/MSP.2021.3106232.

 

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