Mathematical Representation for speech signal based on polynomial equation
Abstract
The sound is an important vital information that it relies to recognize the character when lessening to it. Therefore, the audio signal adopted into many important applications. Sound forming and synthesizing in addition to distinguishing the speaker are so important in fields of digital signal processing. In this paper, work is done to represent acquired acoustic signal based on mathematical techniques. Mathematical representation provides deal with the sound signal which lead to smoothing, amplification or compression, in addition to the sound filtering process. In the proposed algorithm, polynomials of various degrees were adopted as a mathematical representation for speech signal, then the retrieved speech was studied based on level of clarity and the possibility of adopting it as an alternative signal in terms of the proximity to the original sound and the amount of noise added to it. The results shows that the proposed algorithm with degree of polynomial 20 and segment length 25 had the best sound representation and so closed to the original, which clearly seen from the evaluation parameters (Correlation=0.9993, Mean squared error(MSE) =1.32e-06, Standard deviation(STD)=1.80e-05 and the Euclidean dimension(ED) =0.1703).
Keywords
Full Text:
PDFReferences
D. Jurafsky and J. H. Martin, "Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition," ed, 2018.
I. McLoughlin, Applied speech and audio processing: with Matlab examples. Cambridge University Press, 2019.
A. E. Villanueva-Luna et al., De-noising audio signals using MATLAB wavelets toolbox. INTECH Open Access Publisher, 2011.
D. Rocchesso, Introduction to sound processing. Mondo estremo, 2013.
O. J. I. T. o. s. Ghitza and a. processing, "Auditory models and human performance in tasks related to speech coding and speech recognition," vol. 2, no. 1, pp. 115-132, 2014.
A. Gudi, H. J. I. J. o. C. S. Nagaraj, and I. Technology, "Optimal curve fitting of speech signal for disabled children," vol. 1, no. 2, pp. 99-107, 2019.
K. J. I. J. O. S. S. I Alsaif, "Speaker age detection using eigen value," vol. 11, no. 20, pp. 271-290, 2011.
A. Pareek and L. Gidwani, "Measured data of daily global solar irradiation using curve-fitting methods," in 2015 International Conference on Energy Systems and Applications, 2015, pp. 561-565: IEEE.
A. M. Farayola, A. N. Hasan, and A. Ali, "Curve fitting polynomial technique compared to ANFIS technique for maximum power point tracking," in 2017 8th International Renewable Energy Congress (IREC), 2017, pp. 1-6: IEEE.
G. Kiss and K. Vicsi, "Investigation of cross-lingual depression prediction possibilities based on speech processing," in 2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), 2017, pp. 000097-000102: IEEE.
F. Cui, C. Park, and M. J. J. o. e. m. Kim, "Application of curve-fitting techniques to develop numerical calibration procedures for a river water quality model," vol. 249, p. 109375, 2019.
H. I. Younes, "Speaker age detection using eigen values," Master's thesis 2011.
S. Dixit, "Speech Processing: A Review," International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 3, no. 8, 2014.
D. Mallie, "Voice processing using MATLAB as a tool," 2014.
M. t. a. Eraky, "Curve fitting and interpolation techniques," researchgate, 2018.
J. J. J. o. C. Rougier, "Ensemble averaging and mean squared error," vol. 29, no. 24, pp. 8865-8870, 2016.
M. Tektas, "MATLAB EĞRİ UYDURMA (Curve Fitting)," 2020.
"https://www.ni.com/en-lb/innovations/white-papers/08/overview-of-curve-fitting-models-and-methods-in-labview.html," Website 2020
P. Vidyullatha, D. R. J. I. J. o. E. Rao, and C. Engineering, "Machine learning techniques on multidimensional curve fitting data based on R-square and chi-square methods," vol. 6, no. 3, p. 974, 2016.
S. Ahn and J. A. J. E. D. Fessler, The University of Michigan, "Standard errors of mean, variance, and standard deviation estimators," pp. 1-2, 2013.
DOI: https://doi.org/10.33387/protk.v11i2.7352
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Entesar Alasaad
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
| Journal Policies | Submissions | People | Information |
Editorial Office :
Address: Jusuf Abdulrahman 53 Gambesi, Ternate City, Indonesia.
Email: protek@unkhair.ac.id, WhatsApp: +6282292852552
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.