Description and music styles: For building up a ground truth music database the compilation “Now That’s What I Call Music!” (U. Author: Schuller, B., Dorfner, J., Gerhard, R. Annotation (dimensional): Time-continuous arousal and valence annotation (1 Hz). Annotation strategy: Two players from a game used the mouse to annotate the segment over a continuous AV space. Description and music styles: The authors used US pop music to collect time-varying perception of emotions. Author: Kim, Y., Schmidt, E., Emelle, L. Annotation (dimensional): Arousal and valence. On the average, more than ten pairs of AV values are collected from the subjective test for each music sample. The ground truth is set as the mean of the AV values of all subjects tested. Annotation strategy: Subjects (most college students) are asked to listen to a subset of music dataset and to choose two values, each ranges from -1.0 to 1.0 in 11 levels, to indicate their feeling about the AV values of the music sample. Description and music styles: The dataset contains 195 popular songs from Western, Chinese, and Japanese albums. (2006) to be both important and easy to identify, were rated on a scale from one to three (e.g., not happy, neutral, happy). Annotation (categorical): 18 emotions, found by Skowronek et al. Participants were rewarded $10 for a one hour annotation block spent listening to the music. Annotation strategy: The authors paid 66 undergraduate students to annotate the CAL500 corpus with semanticĬoncepts from the vocabulary. Description and music styles: Songs were picked from the authors' personal collection of western popular. Author: Turnbull, D., Barrington, L., Torres, D., Lanckriet G. Annotation (categorical): Cluster 1 (passionate, rousing, confident, boisterous, rowdy), Cluster 2 (rollicking, cheerful, fun, sweet, amiable/good natured), Cluster 3 (literate, poignant, wistful, bittersweet, autumnal, brooding), Cluster 4 (humorous, silly, campy, quirky, whimsical, witty, wry), Cluster 5 (aggressive, fiery, tense/anxious, intense, volatile, visceral). 
Annotation strategy: The pieces were rated by 3 raters, and only a subset of agreement by 2 out of 3 is extracted.
Description and music styles: Selection of the libraries of Associated Production Music (APM), “the world’s leading production music library… offering every imaginable music genre from beautiful classical music recordings to vintage rock to current indie band sounds".Author: Hu, X., Downie, J.S., Laurier, C., Bay, M., Ehmann, A.F.Music Emotion Recognition Datasets DatasetĬategorical (tension, anger, fear, happy, sad, tender) and Dimensional (valence, energy, tension) This website offers a detailed overview of music and emotion datasets and an extended bibliography. We propose a new user-centric conceptualization framework for MER, highlighting where future researchers should focus efforts: (1) open data and experimental reproducibility, (2) inherent subjectivity of concepts and annotations, (3) model explainability and interpretability, (4) cultural and contextual relevance, and (5) ethical implications for MER applications. Our aims are: (1) to provide insights on the typical approaches currently used in the MER workflow, and (2) hint at how previous research directs to specific future directions. It analyzes music in order to computationally predict the emotions perceived by or induced to a listener. In this paper we present 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. Music Emotion Recognition: towards new robust standards in personalized and context-sensitive applications Abstract
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