Emotion Sound Classification with Support Vector Machine Algorithm
Corresponding Author(s) : Chabib Arifin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol 3, No 2, May-2018
Abstract
Speech one of the biometric characteristic owned by human being, as well as fingerprint, DNA, retina of the eyes and so not the two human beings who have the same voice. Human emotion is a matter that can only be predicted through the face of a person, or from the change of facial expression but it turns out human emotions can also be detected through the spoken voice. Someone emotion are happy, angry, neutral, sad, and surprise can be detected through speech signal. The development of voice recognition system is still running at this moment. So that ini this research, the analysis of someone emotion through speech signal. Some related research about the sound aims to have process of identity recognition gender recognition, Emotion recognition based on conversation. In this research the writer does research on the emotional classification of speech two classes started from happy, angry, neutral, sad and surprise while the used algorithm in this research is SVM (Support Vector Machine) with alghoritmMFCC (Mel-frequency cepstral coefficient)for extraction where it contains filter process that adapted to human’s listening. The result of the implementation process of both algorithms gives accuracy level ashappy=68.54%, angry=75.24%, neutral=78.50%, sad=74.22% and surprise=68.23%.
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- Ritu, D. Shah, Dr. Anil, and C. Suthar, “Speech Emotion Recognition Based on SVM Using MATLAB,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2016.
- F. Liqin, M. Xia, and C. Lijiang, “Speaker Independent Emotion Recognition Based on SVM/HMMs Fusion System,” IEEE International Conference on Audio, Language and Image Processing (ICALIP), pages 61-65, 7-9 July 2008.
- R. P. Gadhe, R. R. Deshmukh, and V. B. Waghmare, “KNN Based Emotion Recognition System for Isolated Marathi Speech,” Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad-431004 (MS) India, Vol. 4 No.04 Jul 2015, KINETIK ISSN: 2503-2259; E-ISSN: 2503-2267
- Emotion Sound Classification with Support Vector Machine Algorithm Chabib Arifin, Hartanto Junaedi 99
- N. Thapliyal and G. Amoli, “Speech Based Emotion Recognition with Gaussian Mixture Model,” International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012.
- H. Gang, L. Jiandong, and L. Donghua, “Study of Modulation Recognition Based on HOCs and SVM,” In Proceedings of the 59th Vehicular Technology Conference, VTC 2004-Spring. (Vol. 2, pp. 898–902), 17–19 May 2004.
- P. Shen, Z. Changjun, and X. Chen, “Automatic Speech Emotion Recognition Using Support Vector Machine,” IEEE International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), Volume 2, Page(s): 621 - 625, 12-14, Augustus 2011.
- Sutikyo, and P. Hadi, “Sound Processing Based on Age Using K-Means Method,” Surabaya: Surabaya State Polytechnic of Electronics, Sepuluh November Institute of Technology.
- R. Magdlena, and L. Novamizanti, “Simulation and Analysis of Human Emotion Detection from Speech Sound Based on Discrete Wavelet Transform and Linear Predictive Coding,” Faculty of Telecommunication, Telkom University.
- Bhaskoro, S. Bagas, Ariani, Irna, and A. A. Almsyah, “Transformation of Human Pitch Sound Using PSOLA Method,” ELKOMIKA Journal, Bandung State Institute of Technology, No. 2, Vol. 2, Juy - December 2014.
- B. Yu, H. Li, and C. Fang, “Speech Emotion Recognition based on Optimized Support Vector Machine,” Journal of Software, Vol. 7, No. 12, December 2012.
- A. Rinaldi, Hendra, and D. Alamsyah, “Gender Recognition from Sound Using Support Vector Machine (SVM) Algorithm,’’ Information Engineering Study Program, STMIK GI MDP Palembang.
References
Ritu, D. Shah, Dr. Anil, and C. Suthar, “Speech Emotion Recognition Based on SVM Using MATLAB,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 3, March 2016.
F. Liqin, M. Xia, and C. Lijiang, “Speaker Independent Emotion Recognition Based on SVM/HMMs Fusion System,” IEEE International Conference on Audio, Language and Image Processing (ICALIP), pages 61-65, 7-9 July 2008.
R. P. Gadhe, R. R. Deshmukh, and V. B. Waghmare, “KNN Based Emotion Recognition System for Isolated Marathi Speech,” Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad-431004 (MS) India, Vol. 4 No.04 Jul 2015, KINETIK ISSN: 2503-2259; E-ISSN: 2503-2267
Emotion Sound Classification with Support Vector Machine Algorithm Chabib Arifin, Hartanto Junaedi 99
N. Thapliyal and G. Amoli, “Speech Based Emotion Recognition with Gaussian Mixture Model,” International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 5, July 2012.
H. Gang, L. Jiandong, and L. Donghua, “Study of Modulation Recognition Based on HOCs and SVM,” In Proceedings of the 59th Vehicular Technology Conference, VTC 2004-Spring. (Vol. 2, pp. 898–902), 17–19 May 2004.
P. Shen, Z. Changjun, and X. Chen, “Automatic Speech Emotion Recognition Using Support Vector Machine,” IEEE International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), Volume 2, Page(s): 621 - 625, 12-14, Augustus 2011.
Sutikyo, and P. Hadi, “Sound Processing Based on Age Using K-Means Method,” Surabaya: Surabaya State Polytechnic of Electronics, Sepuluh November Institute of Technology.
R. Magdlena, and L. Novamizanti, “Simulation and Analysis of Human Emotion Detection from Speech Sound Based on Discrete Wavelet Transform and Linear Predictive Coding,” Faculty of Telecommunication, Telkom University.
Bhaskoro, S. Bagas, Ariani, Irna, and A. A. Almsyah, “Transformation of Human Pitch Sound Using PSOLA Method,” ELKOMIKA Journal, Bandung State Institute of Technology, No. 2, Vol. 2, Juy - December 2014.
B. Yu, H. Li, and C. Fang, “Speech Emotion Recognition based on Optimized Support Vector Machine,” Journal of Software, Vol. 7, No. 12, December 2012.
A. Rinaldi, Hendra, and D. Alamsyah, “Gender Recognition from Sound Using Support Vector Machine (SVM) Algorithm,’’ Information Engineering Study Program, STMIK GI MDP Palembang.