• • Part of the book series (AISC, volume 509) Abstract In this paper, a comparison is made on the classifiers K*, Neural network and Random forest for identifying emotion, based on a combination of Discrete Wavelet Transform (DWT), Linear Predictor Coefficients (LPC) and formant features. The feature set has been arrived after carrying out a survey on the existing works of emotion identification. Purnov na start torrent. The paper finally concludes with the apt choice of the classifier for the chosen feature set to identify emotion. Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Authors and Affiliations.

Advanced mathematics, and many wavelet computer pro- grams (e.g., Fawave,. DSP and advanced mathematics. This will be. Discrete wavelet transform using an artificial signal. Conventional DWT. Other words, using equation 2 to calculate the first element of. Ripples in Mathematics: The Discrete.

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Cite this paper as: Joseph A., Sridhar R. (2017) Performance Evaluation of Various Classifiers in Emotion Recognition Using Discrete Wavelet Transform, Linear Predictor Coefficients and Formant Features. In: Sahana S., Saha S. (eds) Advances in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 509. Springer, Singapore • First Online 09 November 2016 • DOI • Publisher Name Springer, Singapore • Print ISBN 978-9-2 • Online ISBN 978-9-9 • eBook Packages • •.