Scientific article
Open access

Developing a benchmark for emotional analysis of music

Published inPloS one, vol. 12, no. 3, e0173392
Publication date2017

Music emotion recognition (MER) field rapidly expanded in the last decade. Many new methods and new audio features are developed to improve the performance of MER algorithms. However, it is very difficult to compare the performance of the new methods because of the data representation diversity and scarcity of publicly available data. In this paper, we address these problems by creating a data set and a benchmark for MER. The data set that we release, a MediaEval Database for Emotional Analysis in Music (DEAM), is the largest available data set of dynamic annotations (valence and arousal annotations for 1,802 songs and song excerpts licensed under Creative Commons with 2Hz time resolution). Using DEAM, we organized the ‘Emotion in Music' task at MediaEval Multimedia Evaluation Campaign from 2013 to 2015. The benchmark attracted, in total, 21 active teams to participate in the challenge. We analyze the results of the benchmark: the winning algorithms and feature-sets. We also describe the design of the benchmark, the evaluation procedures and the data cleaning and transformations that we suggest. The results from the benchmark suggest that the recurrent neural network based approaches combined with large feature-sets work best for dynamic MER.

  • Emotions
  • Algorithms
  • Machine learning algorithms
  • Neural networks
  • Acoustics
  • Music perception
  • Linear regression analysis
  • Swiss National Science Foundation - Ambizione
Citation (ISO format)
ALJANAKI, Anna, YANG, Yi-Hsuan, SOLEYMANI, Mohammad. Developing a benchmark for emotional analysis of music. In: PloS one, 2017, vol. 12, n° 3, p. e0173392. doi: 10.1371/journal.pone.0173392
Main files (1)
Article (Published version)
ISSN of the journal1932-6203

Technical informations

Creation04/26/2017 11:52:00 AM
First validation04/26/2017 11:52:00 AM
Update time03/15/2023 1:38:48 AM
Status update03/15/2023 1:38:47 AM
Last indexation08/30/2023 2:38:15 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack