Open Access
Issue
MATEC Web Conf.
Volume 154, 2018
The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
Article Number 03015
Number of page(s) 5
Section Computer Sciences
DOI https://doi.org/10.1051/matecconf/201815403015
Published online 28 February 2018
  1. R. P. Sharma, O. Farooq, and I. Khan, “Wavelet based sub-band parameters for classification of unaspirated Hindi stop consonants in initial position of CV syllables,” Int. J. Speech Technol., 16, no. 3, pp. 323–332 (2013) [CrossRef] [Google Scholar]
  2. D. Kristomo, R. Hidayat, and I. Soesanti, “Classification of the Syllables Sound Using Wavelet, Renyi Entropy and AR-PSD Features,” in 2017 IEEE 13th International Colloquium on Signal Processing & its Application (CSPA 2017), 13, pp. 97–102 (2017) [Google Scholar]
  3. D. Kristomo, R. Hidayat, and I. Soesanti, “Feature extraction and classification of the Indonesian syllables using Discrete Wavelet Transform and statistical features,” in 2016 2nd International Conference on Science and Technology-Computer (ICST), 2, pp. 88–92 (2016) [CrossRef] [Google Scholar]
  4. S. Hidayat, R. Hidayat, and T. B. Adji, “Speech recognition of CV-patterned indonesian syllable using MFCC, wavelet and HMM,” J. Ilm. Kursor, 8, no. 2, pp. 67–78 (2015) [CrossRef] [Google Scholar]
  5. P. Král, “Discrete Wavelet Transform for automatic speaker recognition,”Image Signal Process. (CISP), 2010 3rd Int. Congr., 7, pp. 3514–3518 (2010) [CrossRef] [Google Scholar]
  6. X. Zhao, Z. Wu, J. Xu, K. Wang, and J. Niu, “Speech Signal Feature Extraction Based on Wavelet Transform,” 2011 Int. Conf. Intell. Comput. Bio-Medical Instrum., no. 1, pp. 179–182 (2011) [Google Scholar]
  7. M. El Ayadi, M. S. Kamel, and F. Karray, “Survey on speech emotion recognition: Features, classification schemes, and databases,” Pattern Recognit., 44, no. 3, pp. 572–587 (2011) [CrossRef] [Google Scholar]
  8. C. Chandra and B. Yegnanarayana, “A constraint satisfaction model for recognition of stop consonantvowel (SCV) utterances,” IEEE Trans. Speech Audio Process., 10, no.7, pp. 472–480, (2002) [CrossRef] [Google Scholar]
  9. G. Dede and M. H. Sazh, “Speech recognition with artificial neural networks,” Digit. Signal Process., 20, no. 3, pp. 763–768 (2010) [CrossRef] [Google Scholar]
  10. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. United States ofAmerica (2009) [Google Scholar]
  11. R. Hidayat, Priyatmadi, and W. Ikawijaya, “Wavelet based feature extraction for the vowel sound,” in 2015 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 1–4 (2015) [Google Scholar]
  12. F. L. Hardjono and R. A. Fox, “Stop Consonant Characteristics: VOT and Voicing in American-Born-Indonesian Children’s Stop Consonants,” The Ohio State University (2011) [Google Scholar]
  13. A. K. Vuppala, K. S. Rao, and S. Chakrabarti, “Spotting and recognition of consonant-vowel units from continuous speech using accurate detection of vowel onset points,” Circuits, Syst. Signal Process., 31, no. 4, pp. 1459–1474 (2012) [CrossRef] [Google Scholar]
  14. D. Kristomo, R. Hidayat, I. Soesanti, and A. Kusjani, “Heart sound feature extraction and classification using autoregressive power spectral density (ARPSD) and statistics features,” in AIP Conference Proceedings, 1755, pp. 90007-1-90007–7 (2016) [Google Scholar]
  15. O. Farooq and S. Datta, “Phoneme recognition using wavelet based features,” Elsevier Inf. Sci., 150, pp. 5–15 (2003) [CrossRef] [Google Scholar]
  16. A. Rényi, “On measures of entropy and information,” in Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, pp. 547–561 (1961) [Google Scholar]
  17. C. Y. Kee, S. G. Ponnambalam, and C. K. Loo, “Binary and multi-class motor imagery using Renyi entropy for feature extraction,” Neural Comput. Appl., pp. 1–12 (2016) [Google Scholar]
  18. N. M. Nawi et al., “The Effect of Pre-Processing Techniques and Optimal Parameters selection on Back Propagation Neural Networks,” 7, no. 3, pp. 770–777 (2017) [Google Scholar]
  19. A. Kuri-Morales, “The Best Neural Network Architecture,” Springer, (2015) [Google Scholar]
  20. R. Kohavi, “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,” Int. Jt. Conf. Artif. Intell., 14, no. 12, pp. 1137–1143 (1995) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.