On instance selection in audio based emotion recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7477 LNAI, Issue , 2012, Pages 186-192

  Meudt, Sascha ^a   Schwenker, Friedhelm ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

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

Indexed keywords

ACTIVE LEARNING; AFFECTIVE COMPUTING; AUDIO ANALYSIS; AUDIO-BASED; BASE CLASSIFIERS; CONFIDENCE VALUES; DATA SETS; EMOTION RECOGNITION; EXPERIMENTAL SETUP; FACIAL EXPRESSIONS; HUMAN COMPUTER INTERFACES; INSTANCE SELECTION; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; NATURAL INTERFACES; NUMERICAL EVALUATIONS; SELECTION METHODS; STATISTICAL EVALUATION; TEMPORAL SCALE; TIME-SCALES; TRAINING SETS; WINDOW SIZE; WIZARD OF OZ;

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; HUMAN COMPUTER INTERACTION; NEURAL NETWORKS; SPEECH RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 84867629933     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33212-8_17     Document Type: Conference Paper

References (19)

1. Bishop, C.: Pattern recognition and machine learning, vol. 4. Springer, New York (2006)
2. Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data Mining and Knowledge Discovery 6(2), 153-172 (2002)
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)
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)
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)
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)
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)
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)
9. Liu, H., Motoda, H.: Instance Selection and Construction for Data Mining. Kluwer Academic Publishers, Norwell (2001)
10. Liu, H., Motoda, H.: On issues of instance selection. Data Mining and Knowledge Discovery, 115-130 (2002)
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)
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)
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)
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)
15. Reinartz, T.: A unifying view on instance selection. Data Mining and Knowledge Discovery 6(2), 191-210 (2002)
16. Russell, J.A., Mehrabian, A.: Evidence for a three-factor theory of emotions. Journal of Research in Personality 11(3), 273-294 (1977)
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)
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)
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/