Fractal Antenna Design using HFSS

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Fractal Antenna Design using HFSS

The project presents speech emotion recognition from speech signals based on features analysis and NN-classifier. Automatic speech emotion recognition (SER) plays an important role in HCI systems for measuring people’s emotions and has dominated psychology by linking expressions to groups of basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). The recognition system involves speech emotion detection, feature extraction and selection, and finally classification. These features are useful to distinguish the maximum number of samples accurately and the NN classifier based on discriminant analysis is used to classify the six different expressions. The simulated results will be shown that the filter-based feature extraction with the used classifier gives much better accuracy with lesser algorithmic complexity than other speech emotion expression recognition approaches.Fractal Antenna Design using HFSS

Existing method

  • Principal Component Analysis
  • Geometric methods.


  • Low discriminatory power and high computational load
  • In geometric-based methods, the geometric features like the distance between speech signals.

Proposed method

Speech Emotion recognition for transform features system through textural analysis and NN classifier. The system involves,


  • Speech Signal
  • Features Extraction using mfcc
  • NN Classifier


  • Robustness to illumination changes
  • Low complexity
  • High discriminatory power


  • Border security system
  • Robotics and Computer games
  • Machine vision

Software Required

  • Python idle above 3.0


[1] T. U. Binbin, “Speech emotion recognition based on improved fcc with and,” Computer Engineering and Applications, vol. 48, no. 18, pp. 119-122, July 2012.

[2] H. Yao, Y. Sun, and X. Zhang, “Research on no? near dynamics features of emotional speech,” Journal of Xidian University(Natural Science), October 2016.

[3] S. Ying, Y. Hui, X. Zhang, and Q. Zhang, “Feature extraction of emotional speech based on chaotic characteristics,” Journal of Tianjin University, vol. 48, no. 8, pp. 681-685, August 2015.

[4] Y. E. Jixiang, “Speech emotion recognition based on multifractal,” Computer Engineering and Applications, vol. 48, no. 13, pp. 186-189, 2012.

[5] S. Kuchibhotla, H. D. Vankayalapati, and K. R. Anne, “An optimal two-stage feature selection for speech emotion recognition using acoustic features,” International Journal of Speech Technology, vol. 19, no. 4, pp. 1-11, August 2016.

[6] I. Trabelsi and M. S. Bouhlel, “Feature Selection for GUMI KernelBased SVM in Speech Emotion Recognition;’International Journal of Synthetic Emotions, pp. 57-68, August 2016.

[7] Y. Sun and G. Wen, “Emotion recognition using semi-supervised feature selection with speaker normalization,” Springer-Verlag New York, Inc., September 2015.

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