Towards Emotion Recognition: A Persistent Entropy Application

  • Rocio Gonzalez-Diaz
  • Eduardo Paluzo-Hidalgo
  • José F. Quesada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11382)


Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust and surprised).


Persistent homology Persistent entropy Emotion recognition Support vector machine 



This research has been partially supported by MINECO, FEDER/UE under grant MTM2015-67072-P. We thank the anonymous reviewers for their valuable comments.


  1. 1.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: COLT 1992, pp. 144–152. ACM, New York (1992)Google Scholar
  2. 2.
    Bredon, G.: Topology and Geometry. Springer, New York (1993). Scholar
  3. 3.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). Scholar
  4. 4.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, 1st edn. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  5. 5.
    Edelsbrunner, H., Harer, J.L.: Computational Topology, an Introduction. American Mathematical Society, Providence (2010)zbMATHGoogle Scholar
  6. 6.
    Geron, A.: Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, Sebastopol (2017)Google Scholar
  7. 7.
    Globerson, E., Amir, N., Golan, O., Kishon-Rabin, L., Lavidor, M.: Psychoacoustic abilities as predictors of vocal emotion recognition. Atten. Percept. Psychophys. 75(8), 1799–1810 (2013)CrossRefGoogle Scholar
  8. 8.
    Howard, D.M., Angus, J.: Acoustics and Psychoacoustics, 2nd edn. Butterworth-Heinemann, Newton (2000)CrossRefGoogle Scholar
  9. 9.
    Livingstone, S.R., Russo, F.A.: The Ryerson audio-visual database of emotional speech and song (RAVDESS): a dynamic, multimodal set of facial and vocal expressions in North American English. PLOS ONE 13(5), 1–35 (2018)CrossRefGoogle Scholar
  10. 10.
    Ortony, A., Turner, T.J.: What’s basic about basic emotions? Psychol. Rev. 97(3), 315 (1990)CrossRefGoogle Scholar
  11. 11.
    Pearson, K.: Note on regression and inheritance in the case of two parents. Proc. R. Soc. Lond. 58, 240–242 (1895)CrossRefGoogle Scholar
  12. 12.
    Popova, A.S., Rassadin, A.G., Ponomarenko, A.A.: Emotion recognition in sound. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V. (eds.) NEUROINFORMATICS 2017. SCI, vol. 736, pp. 117–124. Springer, Cham (2018). Scholar
  13. 13.
    Rucco, M., et al.: A new topological entropy-based approach for measuring similarities among piecewise linear functions. Signal Process. 134, 130–138 (2017)CrossRefGoogle Scholar
  14. 14.
    Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)CrossRefGoogle Scholar
  15. 15.
    Schuller, B., Batliner, A.: Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing. Wiley, Hoboken (2013)CrossRefGoogle Scholar
  16. 16.
    Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. Speech Commun. 48, 1162–1181 (2006)CrossRefGoogle Scholar
  17. 17.
    Wasserman, L.: Topological data analysis. Ann. Rev. Stat. Appl. 5(1), 501–532 (2018)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Yang, B., Lugger, M.: Emotion recognition from speech signals using new harmony features. Signal Process. 90(5), 1415–1423 (2010). Special Section on Statistical Signal & Array ProcessingCrossRefGoogle Scholar
  19. 19.
    Zhang, B., Essl, G., Mower Provost, E.: Recognizing emotion from singing and speaking using shared models, September 2015.
  20. 20.
    Zomorodian, A., Carlsson, G.: Computing persistent homology. Discret. Comput. Geom. 33(2), 249–274 (2005)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rocio Gonzalez-Diaz
    • 1
  • Eduardo Paluzo-Hidalgo
    • 1
  • José F. Quesada
    • 2
  1. 1.Department of Applied Mathematics IUniversity of SevilleSevilleSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of SevilleSevilleSpain

Personalised recommendations