Emotion Recognition

Overview

The development of systems for automatic content-based music emotion recognition spans a wide breadth of areas including psychology, signal processing, and machine learning.

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Quick Links

Get the MoodSwings Turk Dataset Here
Emotion in Music Database (1000 Songs)

Theses:

Schmidt, E. M. (2012). Modeling and Predicting Emotion in Music. Unpublished Ph.D. Thesis, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA. [PDF]

Published Work:

  • M. Soleymani, M. Caro, E. M. Schmidt, C. Sha, Y. Yang. 1000 Songs for Emotional Analysis of Music. Proceedings of the ACM multimedia 2013 workshop on Crowdsourcing for Multimedia. ACM, ACM , 2013.
  • Schmidt, E. M., Scott, J., and Kim, Y. E. (2012). Feature Learning in Dynamic Environments: Modeling the Acoustic Structure of Musical Emotion. Proceedings of the 2012 International Society for Music Information Retrieval Conference, Porto, Portugal: ISMIR.
  • Schmidt, E. M., Prockup, M., Scott, J., Dolhansky, B., Morton, B. and Kim, Y. E. (2012). Relating perceptual and feature space invariances in music emotion recognition. Proceedings of the International Symposium on Computer Music Modeling and Retrieval, London, U.K.: CMMR. Best Student Paper. [PDF] [Oral Presentation]
  • Scott, J., Schmidt, E. M., Prockup, M., Morton, B. and Kim, Y. E. (2012). Predicting time-varying musical emotion distributions from multi-track audio. Proceedings of the International Symposium on Computer Music Modeling and Retrieval, London, U.K.: CMMR. [PDF]
  • Schmidt, E. M. and Kim, Y. E. (2012). Modeling and Predicting Emotion in Music. Music, Mind, and Invention Workshop, Ewing, NJ: MMI. [PDF]
  • Schmidt, E. M. and Kim, Y. E. (2011). Modeling the acoustic structure of musical emotion with deep belief networks. NIPS Workshop on Music and Machine Learning, Sierra Nevada, Spain: NIPS-MML. [Oral Presentation]
  • Schmidt, E. M. and Kim, Y. E. (2011). Modeling musical emotion dynamics with conditional random fields. Proceedings of the 2011 International Society for Music Information Retrieval Conference, Miami, Florida: ISMIR. [PDF]
  • Speck, J. A., Schmidt, E. M., Morton, B. G., and Kim, Y. E. (2011). A comparative study of collaborative vs. traditional annotation methods. Proceedings of the 2011 International Society for Music Information Retrieval Conference, Miami, Florida: ISMIR. [PDF]
  • Schmidt, E. M. and Kim, Y. E. (2011). Learning emotion-based acoustic features with deep belief networks. Proceedings of the 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY: WASPAA. [PDF]
  • Kim, Y. E., Batula, A. M., Migneco, R., Richardson, P., Dolhansky, B., Grunberg, D., Morton, B., Prockup, M., Schmidt, E. M., and Scott, J. (2011). Teaching STEM concepts through music technology and DSP. Proceedings of the 14th IEEE Digital Signal Processing Workshop and 6th IEEE Signal Processing Education Workshop, Sedona, AZ: DSP/SPE. [PDF]
  • Schmidt, E. M. and Kim, Y. E. (2010). Prediction of time-varying musical mood distributions using Kalman filtering. Proceedings of the 2010 IEEE International Conference on Machine Learning and Applications, Washington, D.C.: ICMLA. [PDF]
  • Schmidt, E. M. and Kim, Y. E. (2010). Prediction of time-varying musical mood distributions from audio. Proceedings of the 2010 International Society for Music Information Retrieval Conference, Utrecht, Netherlands: ISMIR. [PDF]
  • Morton, B. G., Speck, J. A., Schmidt, E. M., and Kim, Y. E. (2010). Improving music emotion labeling using human computation. Proceedings of the ACM SIGKDD Workshop on Human Computation, Washington, D.C.: HCOMP [PDF]
  • Schmidt, E. M., Turnbull, D., and Kim, Y. E. (2010). Feature selection for content-based, time-varying musical emotion regression. Proc. ACM SIGMM International Conference on Multimedia Information Retrieval, Philadelphia, PA. [PDF]
  • Schmidt, E. M. and Kim, Y. E. (2009). Projection of acoustic features to continuous valence-arousal mood labels via regression. Accepted to the 2009 International Society for Music Information Retrieval Conference, Kobe, Japan: ISMIR. [PDF]
  • Kim, Y. E., Schimdt, E., and Emelle, L. (2008). Moodswings: a collaborative game for music mood label collection. Proceedings of the 2008 International Conference on Music Information Retrieval, Philadelphia, PA: ISMIR. [PDF]