Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

IAPR Workshop on Artificial Neural Networks in Pattern Recognition

ANNPR 2012: Artificial Neural Networks in Pattern Recognition pp 186–192Cite as

  1. Home
  2. Artificial Neural Networks in Pattern Recognition
  3. Conference paper
On Instance Selection in Audio Based Emotion Recognition

On Instance Selection in Audio Based Emotion Recognition

  • Sascha Meudt &
  • Friedhelm Schwenker 
  • Conference paper
  • 1315 Accesses

  • 7 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7477)

Abstract

Affective computing aim to provide simpler and more natural interfaces for human-computer interaction applications, e.g. recognizing automatically the emotional status of the user based on facial expressions or speech is important to model user as complete as possible in order to develop human-computer interfaces that are able to respond to the user’s action or behavior in an appropriate manner. In this paper we focus on audio-based emotion recognition. Data sets employed for the statistical evaluation have been collected through Wizard-of-Oz experiments. The emotional labels have been are defined through the experimental set up therefore given on a relatively coarse temporal scale (a few minutes) which This global labeling concept might lead to miss-labeled data at smaller time scales, for instance for window sizes uses in audio analysis (less than a second). Manual labeling at these time scales is very difficult not to say impossible, and therefore our approach is to use the globally defined labels in combination with instance/sample selection methods. In such an instance selection approach the task is to select the most relevant and discriminative data of the training set by using a pre-trained classifier. Mel-Frequency Cepstral Coefficients (MFCC) features are used to extract relevant features, and probabilistic support vector machines (SVM) are applied as base classifiers in our numerical evaluation. Confidence values to the samples of the training set are assigned through the outputs of the probabilistic SVM.

Keywords

  • Emotion Recognition
  • Human Computer Interaction
  • Instance Selection
  • Active Learning

Download conference paper PDF

References

  1. Bishop, C.: Pattern recognition and machine learning, vol. 4. Springer, New York (2006)

    MATH  Google Scholar 

  2. Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6(2), 153–172 (2002)

    CrossRef  MathSciNet  MATH  Google Scholar 

  3. Domingo, C., Gavaldà, R., Watanabe, O.: Adaptive sampling methods for scaling up knowledge discovery algorithms. Data Mining and Knowledge Discovery 6(2), 131–152 (2002)

    CrossRef  MathSciNet  MATH  Google Scholar 

  4. Esparza, J., Scherer, S., Brechmann, A., Schwenker, F.: Automatic emotion classification vs. human perception: Comparing machine performance to the human benchmark. In: International Conference on Information Science, Signal Processing and Their Applications (ISSPA 2012), pp. 1286–1291 (2012)

    Google Scholar 

  5. Esparza, J., Scherer, S., Schwenker, F.: Studying Self- and Active-Training Methods for Multi-feature Set Emotion Recognition. In: Schwenker, F., Trentin, E. (eds.) PSL 2011. LNCS, vol. 7081, pp. 19–31. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  6. Glodek, M., Tschechne, S., Layher, G., Schels, M., Brosch, T., Scherer, S., Kchele, M., Schmidt, M., Neumann, H., Palm, G., Schwenker, F.: Multiple classifier systems for the classification of audio-visual emotional states. In: 1st International Audio/Visual Emotion Challenge and Workshop (2011)

    Google Scholar 

  7. de Haro-García, A., García-Pedrajas, N., del Castillo, J.A.R.: Large scale instance selection by means of federal instance selection. Data Mining and Knowledge Engineering 75, 58–77 (2012)

    CrossRef  Google Scholar 

  8. Kelley, J.: An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Information Systems (TOIS) 2(1), 26–41 (1984)

    CrossRef  MathSciNet  Google Scholar 

  9. Liu, H., Motoda, H.: Instance Selection and Construction for Data Mining. Kluwer Academic Publishers, Norwell (2001)

    Google Scholar 

  10. Liu, H., Motoda, H.: On issues of instance selection. Data Mining and Knowledge Discovery, 115–130 (2002)

    Google Scholar 

  11. Madigan, D., Raghavan, N., DuMouchel, W., Nason, M., Posse, C., Ridgeway, G.: Likelihood-based data squashing: A modeling approach to instance construction. Data Mining and Knowledge Discovery 6(2), 173–190 (2002)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Meudt, S., Bigalke, L., Schwenker, F.: ATLAS – an annotation tool for HCI data utilizing machine learning methods. In: Proceedings of the 4th Internantional Conference on Applied Human Factors and Ergonomics, AHFE 2012 (in print, 2012)

    Google Scholar 

  13. Olvera-Lpez, J.A., Carrasco-Ochoa, J.A., Trinidad, J.F.M., Kittler, J.: A review of instance selection methods. Artificial Intelligence Reviews, 133–143 (2010)

    Google Scholar 

  14. Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers 10(3), 61–74 (1999)

    Google Scholar 

  15. Reinartz, T.: A unifying view on instance selection. Data Mining and Knowledge Discovery 6(2), 191–210 (2002)

    CrossRef  MathSciNet  MATH  Google Scholar 

  16. Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. Journal of Research in Personality 11(3), 273–294 (1977)

    CrossRef  Google Scholar 

  17. Schels, M., Kächele, M., Hrabal, D., Walter, S., Traue, H.C., Schwenker, F.: Classification of Emotional States in a Woz Scenario Exploiting Labeled and Unlabeled Bio-physiological Data. In: Schwenker, F., Trentin, E. (eds.) PSL 2011. LNCS, vol. 7081, pp. 138–147. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  18. Scherer, S., Glodek, M., Schwenker, F., Campbell, N., Palm, G.: Spotting laughter in natural multiparty conversations: A comparison of automatic online and offline approaches using audiovisual data. TiiS 2(1), 4 (2012)

    CrossRef  Google Scholar 

  19. Walter, S., Scherer, S., Schels, M., Glodek, M., Hrabal, D., Schmidt, M., Böck, R., Limbrecht, K., Traue, H.C., Schwenker, F.: Multimodal Emotion Classification in Naturalistic User Behavior. In: Jacko, J.A. (ed.) HCII 2011, Part III. LNCS, vol. 6763, pp. 603–611. Springer, Heidelberg (2011), http://www.springerlink.com/content/606237v0u5225w50/

    CrossRef  Google Scholar 

Download references

Authors
  1. Sascha Meudt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Friedhelm Schwenker
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler (FBK), 38123, Trento, Italy

    Nadia Mana

  2. Institute of Neural Information Processing, University of Ulm, 89069, Ulm, Germany

    Friedhelm Schwenker

  3. Dipartimento di Ingegneria dell’Informazione, Università di Siena, 53100, Siena, Italy

    Edmondo Trentin

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Meudt, S., Schwenker, F. (2012). On Instance Selection in Audio Based Emotion Recognition. In: Mana, N., Schwenker, F., Trentin, E. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2012. Lecture Notes in Computer Science(), vol 7477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33212-8_17

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33212-8_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33211-1

  • Online ISBN: 978-3-642-33212-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature