The PMEmo Dataset for Music Emotion Recognition

Abstract

Music Emotion Recognition (MER) has recently received considerable attention. To support the MER research which requires large music content libraries, we present the PMEmo dataset containing emotion annotations of 794 songs as well as the simultaneous electrodermal activity (EDA) signals. A Music Emotion Experiment was well-designed for collecting the affective-annotated music corpus of high quality, which recruited 457 subjects. The dataset is publically available to the research community, which is foremost intended for benchmarking in music emotion retrieval and recognition. To straightforwardly evaluate the methodologies for music affective analysis, it also involves pre-computed audio feature sets. In addition to that, manually selected chorus excerpts (compressed in MP3) of songs are provided to facilitate the development of chorus-related research. In this article, We describe in detail the resource acquisition, subject selection, experiment design and annotation collection procedures, as well as the dataset content and data reliability analysis. We also illustrate its usage in some simple music emotion recognition tasks which testified the PMEmo dataset's competence for the MER work. Compared to other homogeneous datasets, PMEmo is novel in the organization and management of the recruited annotators, and it is also characterized by its large amount of music with simultaneous physiological signals.