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All issues Volume 43 (2022) ITM Web Conf., 43 (2022) 01019 References
Open Access
Issue
ITM Web Conf.
Volume 43, 2022
The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
Article Number 01019
Number of page(s) 6
DOI https://doi.org/10.1051/itmconf/20224301019
Published online 14 March 2022
  1. J. Parkinson, ” An essay on the shaking palsy. London: Whittingham and Rowland, 1817”. Classics in neurology. Huntington, NY: Robert E. Krieger Publishing Co lnc, 1971, p. 158-191. [Google Scholar]
  2. M. Hireš, M. Gazda, P. Drotár, N. D. Pah, M. A Motin and D. K. Kumar. “Convolutional neural network ensemble for Parkinson’s disease detection from voice recordings”. Computers in biology and medicine, 2021, p. 105021. [Google Scholar]
  3. J. L. Manes, E. Herschel, K. Aveni, K. Tjaden, T. Parrish, T. Simuni, … and A. C. Roberts. “The effects of a simulated fMRI environment on voice intensity in individuals with Parkinson’s disease hypophonia and older healthy adults”. Journal of Communication Disorders, 2021, vol. 94, p. 106149. [CrossRef] [Google Scholar]
  4. K. Wrobel. “Diagnosing Parkinson’s disease by means of ensemble classification of patients’ voice samples”. Procedia Computer Science, 2021, vol. 192, p. 3905-3914. [CrossRef] [Google Scholar]
  5. D. Meghraoui, B. Boudraa, T. Merazi and P. G. Vilda. “A novel pre-processing technique in pathologic voice detection: Application to Parkinson’s disease phonation”. Biomedical Signal Processing and Control, 2021, vol. 68, p. 102604. [CrossRef] [Google Scholar]
  6. S. S. Upadhya and A. N Cheeran. “Discriminating Parkinson and healthy people using phonation and cepstral features of speech”. Procedia computer science, 2018, vol. 143, p. 197-202. [CrossRef] [Google Scholar]
  7. R. Chiaramonte and M. Bonfiglio. “Acoustic analysis of voice in Parkinson’s disease: a systematic review of voice disability and meta-analysis of studies”. Revista de neurologia, 2020, vol. 70, no 11, p. 393-405. [CrossRef] [Google Scholar]
  8. Y. E. Huh, J. Park, M. K. Suh, S. E Lee, J. Kim, Y Jeong, … and J.W. Cho. “Differences in early speech patterns between Parkinson variant of multiple system atrophy and Parkinson’s disease”. Brain and language, 2015, vol. 147, p. 14-20. [CrossRef] [Google Scholar]
  9. S. S. Upadhya, A. N. Cheeran, and J. H. Nirmal. “Thomson Multitaper MFCC and PLP voice features for early detection of Parkinson disease”. Biomedical Signal Processing and Control, 2018, vol. 46, p. 293-301. [CrossRef] [Google Scholar]
  10. Q. W. Oung, S. N. Basah, H. Muthusamy, V. Vijean and H. Lee. “Evaluation of short-term cepstral based features for detection of Parkinson’s Disease severity levels through speech signals”. In: IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2018. p. 012039. [CrossRef] [Google Scholar]
  11. A. Benba, A Jilbab and A. Hammouch. “Detecting patients with Parkinson’s disease using Mel frequency cepstral coefficients and support vector machines”. International Journal on Electrical Engineering and Informatics, 2015, vol. 7, no 2, p. 297. [CrossRef] [Google Scholar]
  12. A. Benba, A. Jilbab, and A. Hammouch.” Discriminating between patients with Parkinson’s and neurological diseases using cepstral analysis”. IEEE transactions on neural systems and rehabilitation engineering, 2016, vol. 24, no 10, p. 1100-1108. [CrossRef] [Google Scholar]
  13. T. B. Drissi, S. Zayrit, B Nsiri and A. Ammoummou. “Diagnosis of Parkinson’s disease based on wavelet transform and Mel frequency cepstral coefficients”. Int. J. Adv. Comput. Sci. Appl, 2019, vol. 10, p. 125-132. [Google Scholar]
  14. M. Farrús and J. Codina-Filbà. “Combining prosodic, voice quality and lexical features to automatically detect Alzheimer’s disease”. arXiv preprint arXiv:2011.09272, 2020. [Google Scholar]
  15. P. Vizza, P, and G. Tradigo.” On the analysis of biomedical signals for disease classification”. ACM SIGBioinformatics Record, 2019, vol. 8, no 3, p. 7-10. [CrossRef] [Google Scholar]
  16. S. Mirzaei, M. El Yacoubi, S. Garcia-Salicetti, J. Boudy, C. Kahindo, V. Cristancho-Lacroix, … and A. S. Rigaud. “Two-stage feature selection of voice parameters for early Alzheimer’s disease prediction”. Irbm, 2018, vol. 39, no 6, p. 430-435. [CrossRef] [Google Scholar]
  17. R. Frail, J.I. Godino-Llorente, N. Saenz-Lechon, V. Osma-Ruiz and C. Fredouille. “MFCC-based remote pathology detection on speech transmitted through the telephone channel”. Proc Biosignals, 2009. [Google Scholar]
  18. A. Meghanani, C. S Anoop, A. G. Ramakrishnan. “An exploration of log-Mel spectrogram and MFCC features for Alzheimer’s dementia recognition from spontaneous speech”. In: 2021 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2021. p. 670-677. [CrossRef] [Google Scholar]
  19. J. V. E. López, L. Tóth, I. Hoffmann, J. Kálmán, M. Pákáski and G. Gosztolya. “Assessing Alzheimer’s disease from speech using the i-vector approach”. In: International Conference on Speech and Computer. Springer, Cham, 2019. p. 289-298. [CrossRef] [Google Scholar]
  20. H. Mukherjee,S. M. Obaidullah, K. C. Santosh, S. Phadikar and K. Roy. “A lazy learning-based language identification from speech using MFCC-2 features”. International Journal of Machine Learning and Cybernetics, 2020, vol. 11, no 1, p. 1-14. [CrossRef] [Google Scholar]
  21. K. Sarmah and U. Bhattacharjee. “GMM based Language Identification using MFCC and SDC Features”. International Journal of Computer Applications, 2014, vol. 85, no 5. [Google Scholar]
  22. J. C. Liu, F. Y. Leu, G. L. Lin, H. Susanto. “An MFCC‐based text‐independent speaker identification system for access control”. Concurrency and Computation: Practice and Experience, 2018, vol. 30, no 2, p. e4255. [CrossRef] [Google Scholar]
  23. F. Y. Leu and G. L. Lin.” An MFCC-based speaker identification system”. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA). IEEE, 2017. p. 1055-1062. [CrossRef] [Google Scholar]
  24. M. S. Likitha, S. R. R. Gupta, K. Hasitha, and A. U. Raju. “Speech-based human emotion recognition using MFCC”. In: 2017 international conference on wireless communications, signal processing, and networking (WiSPNET). IEEE, 2017. p. 2257-2260. [CrossRef] [Google Scholar]
  25. P. P. Dahake, K. Shaw, P. Malathi. “Speaker dependent speech emotion recognition using MFCC and Support Vector Machine”. In: 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). IEEE, 2016. p. 1080-1084. [CrossRef] [Google Scholar]
  26. N. J. Nalini, S. Palanivel.” Music emotion recognition: The combined evidence of MFCC and residual phase”. Egyptian Informatics Journal, 2016, vol. 17, no 1, p. 1-10. [CrossRef] [Google Scholar]
  27. B. E. Sakar, M. E. Isenkul, C. O. Sakar, A. Sertbas, F. Gurgen, S. Delil, … and O. Kursun. “Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings”. IEEE Journal of Biomedical and Health Informatics, 2013, vol. 17, no 4, p. 828-834. [CrossRef] [Google Scholar]
  28. N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, … and H. H. Liu. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis”. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 1998, vol. 454, no 1971, p. 903-995. [CrossRef] [Google Scholar]
  29. Kamil Wojcicki (2021). HTK MFCC MATLAB (https://www.mathworks.com/matlabcentral/fileexchange/32849-htk-mfcc-matlab), MATLAB Central File Exchange. Retrieved December 13, 2021. [Google Scholar]

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