WSEAS Transactions on Signal Processing


Print ISSN: 1790-5052
E-ISSN: 2224-3488

Volume 13, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.


Accurate Power Spectrum Estimation of Speech with Spectrum Compensation Based on Prediction Error Filtering

AUTHORS: Md Arifour Rahman, Yosuke Sugiura, Tetsuya Shimamura

Download as PDF

ABSTRACT: This paper proposes a linear prediction (LP) method to estimate accurately the original power spectrum of the input speech signal. A prediction error filter (PEF) is used as a pre-processor, and the LP based power spectrum estimation is compensated by the frequency characteristics of the designed PEF. Through experiments on synthetic vowels, we show that the proposed spectrum compensation method can estimate the power spectrum more accurately than the direct and pre-emphasis LP methods.

KEYWORDS: Linear prediction, prediction error filter, formant frequency, spectrum compensation

REFERENCES:

[1] S. Haykin, Adaptive Filter Theory, PrenticeHall, 2002.

[2] S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, Wiley, 2009.

[3] J. Makhoul, Linear Prediction : A Tutorial Review, Proc. IEEE, Vol. 63, No. 4, 1975, pp. 561- 580.

[4] J. D. Markel, Digital Inverse Filtering- a New Tool for Format Trajectory Estimation, IEEE Trans. Audio Electroacoust., Vol. AU-20, No. 2, 1972, pp. 129-137.

[5] B. S. Atal and S. Hanauer, Speech Analysis and Synthesis by Linear Prediction of the Speech Wave, J. Acoust. Soc. Amer., Vol. 50, No. 2, 1974, pp. 637-655.

[6] S. Chandra and W. C. Lin, Experimental Comperison Between Stationary and Nonstationary Formulations of Linear Prediction Applied to Voiced Speech Analysis, IEEE Trans. on Acoust., Speech, Signal Processina, Vol. ASSP- 22, No. 6, 1974, pp.403-415.

[7] P. Kabal, Ill-Conditioning and Bandwidth Expansion in Linear Prediction of Speech, Proc. IEEE Int. Conf. on Acoust., Speech and Signal Processing Acoust, 2003, pp. 824-827.

[8] S. V. Parter, On the Extreme Eigenvalues of Truncated Toeplitz Matrices. Bulletin of Amer. Math. Soc., Vol. 67, 1961, pp. 191-196.

[9] H. Kesten, On the Extreme Eigenvalues of Translation Kernels and Toeplitz Matrices, J. d’Analyse Math., Vol. 10, 1962, pp. 117-138.

[10] G. Fant, J. Liljencrants and Q. G. Lin, A Four Parameter Model of Glottal Flow, Quart. Progress and Status Rep., Speech Transmission Lab, Royal Inst. Technol., 1985, pp. 1-13.

[11] H. Strik, Automatic Parameterization of Differentiated Glottal Flow: Comparing Methods by Means of Synthetic Flow Pulses, J. Acoust. Soc. Amer., Vol. 103, No. 5, 1998, pp. 2659-2669.

WSEAS Transactions on Signal Processing, ISSN / E-ISSN: 1790-5052 / 2224-3488, Volume 13, 2017, Art. #3, pp. 21-25


Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

Bulletin Board

Currently:

The editorial board is accepting papers.


WSEAS Main Site