State of the Art Report: Music Emotion Recognition: A State of the Art Review

By Youngmoo E. Kim, Erik M. Schmidt, Raymond Migneco, Brandon G. Morton, Patrick Richardson, Jeffrey J. Scott, Jacquelin A. Speck, Douglas R. Turnbull. International Society for Music Information Retrieval Conference, 2010

332 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a977808a4a56d6adf0cd0bb011638ed8d2d97b99

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Xavier Favory
2. Xavier Serra
3. Eduardo Fonseca
4. Frederic Font
5. Bob L. Sturm
6. Erik M. Schmidt
7. Youngmoo E. Kim
8. Jordi Pons

Citations in the corpus

• Learning emotion-based acoustic features with deep belief networks (2011, background): “The problem of automated recognition of emotional content (mood) within music has been the subject of increasing attention among the music information retrieval (Music-IR) research community [1].”
• A Simple Method to Determine if a Music Information Retrieval System is a “Horse” (2014, background): “As emotion in music can be vague for humans [24], the success criteria for such synchrony could be less strict than that for synchrony in time.”
• Search Result Clustering in Collaborative Sound Collections (2020, methodology): “Combining these different types of features enables the representation of the timbre of musical instruments or more high-level characteristics such as music moods and genres [11, 18, 23, 31, 41].”