@article {2023,
	title = {MERP: A Music Dataset with Emotion Ratings and Raters{\textquoteright} Profile Information},
	journal = {Sensors},
	volume = {23},
	year = {2023},
	month = {12/2022},
	pages = {1-23},
	abstract = {Abstract: Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence-arousal space. Four additional songs were selected from the DEAM dataset to act as a benchmark in this study and filter out low quality ratings. A total of 452 participants participated in annotating the dataset, with 277 participants remaining after thoroughly cleaning the dataset. Their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.},
	keywords = {emotion prediction; music; music emotion dataset; affective computing},
	doi = {https://doi.org/10.3390/s23010382},
	url = {https://www.mdpi.com/1424-8220/23/1/382},
	author = {En Yan Koh and Kin Wai Cheuk and Kwan Yee Heung and Kat R. Agres and Dorien Herremans}
}
