Classification of Multi-variate Varying Length Time Series Using Descriptive Statistical Features

  • S. Chandrakala
  • C. Chandra Sekhar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


Classification of multi-variate time series data of varying length finds applications in various domains of science and technology. There are two paradigms for modeling multi-variate varying length time series, namely, modeling the sequences of feature vectors and modeling the sets of feature vectors in the time series. In tasks such as text independent speaker recognition, audio clip classification and speech emotion recognition, modeling temporal dynamics is not critical and there may not be any underlying constraint in the time series. Gaussian mixture models (GMM) are commonly used for these tasks. In this paper, we propose a method based on descriptive statistical features for multi-variate varying length time series classification. The proposed method reduces the dimensionality of representation significantly and is less sensitive to missing samples. The proposed method is applied on speech emotion recognition and audio clip classification. The performance is compared with that of the GMMs based approaches that use maximum likelihood method and variational Bayes method for parameter estimation, and two approaches that combine GMMs and SVMs, namely, score vector based approach and segment modeling based approach. The proposed method is shown to give a better performance compared to all other methods.


Time series classification Descriptive statistical features Speech emotion recognition Audio clip classification 


  1. 1.
    Rabiner, L., Huang, B.-H.: Fundamentals of speech recognition. Prentice Hall, NewYork (1993)Google Scholar
  2. 2.
    Mishra, H.K., Sekhar, C.C.: Variational Gaussian mixture models for speech emotion recognition. In: International Conference on Advances in Pattern Recognition, Kolkata, India (February 2009)Google Scholar
  3. 3.
    Vapnik, V.: Statistical learning Theory. Wiley-Interscience, New York (1998)zbMATHGoogle Scholar
  4. 4.
    Chandrakala, S., Sekhar, C.C.: Combination of generative models and SVM based classifier for speech emotion recognition. In: Proc. Int. Joint Conf. Neural Networks, Atlanta, Georgia (June 2009)Google Scholar
  5. 5.
    Burkhardt, F., Paeschke, A., Rolfes, M., Weiss, W.S.B.: A database of German emotional speech. In: Interspeech, Lisbon, Portugal, pp. 1517–1520 (2005)Google Scholar
  6. 6.
    Sato, N., Obuchi, Y.: Emotion recognition using Mel-frequency cepstral coefficients. Journal of Natural Language Processing 14(4), 83–96 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • S. Chandrakala
    • 1
  • C. Chandra Sekhar
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyMadrasIndia

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