e-ISSN: 2582-5208 International Research Journal of Modernization in Engineering Technology and Science Volume:03/Issue:03/March-2021
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SPEECH ANALYSIS AND DENOISING USING WAVELETS Palle Praneeth*1, Parankusham Ramyaja Shreeya*2 *1PG
Scholar, Department of Electronics & Communication Engineering, JNTUH College of Engineering, Hyderabad, Telangana, India.
*2PG
Scholar, Department of Electronics & Communication Engineering, JNTUH College of Engineering, Hyderabad, Telangana, India.
ABSTRACT All long-distance speech communications (in planes, helicopters, trains etc.,) have distortions in signal due to noise. So, analysis of speech signal and removal of noise plays an important role in communication. In this paper we discuss about how to denoise the signal using Discrete Wavelet Transform (DWT). Although there are many ways of denoising by using FT, FFT, STFT etc., there are certain limitations to them because of which we prefer denoising the signal using DWT. In DWT we do denoising using soft thresholding (ST), where values for positive and negative coefficients are shrinked towards zero. Keywords: Discrete wavelet Transform, Thresholding, Wavelet Decomposition.
I.
INTRODUCTION
The Wavelet transform provides the time and frequency information simultaneously. So more detailed analysis of signal is possible and optimistic results can be achieved. As in its continuous form it produces huge data and computational work, to reduce them the Discrete Wavelet Transform is introduced. Here equation(1) represents Mother Wavelet Function, where a is scaling parameter, b is translation parameter, equation(2) represents Continuous Wavelet Transform(CWT), equation(3) represents Discrete Wavelet Transformation.
( )
√
((
) )…………. eq (1)
( )>=∫
CWT(a,b)=<f(x),
a=
( )
( )
( )
………eq (2)
, where
b=n
where ( )
DWT(m,n)=<f,
(
(
>=
))
∑
( )
(
)………eq (3)
There are many different types of Wavelets and they are chosen as per the requirement. Here we divide speech signal into different frequencies and study them and apply Soft thresholding and reconstruct the signal. Noise signal which has low Lipschitz index and different singularities is observed based on which universal threshold value is calculated to denoise.
II.
METHODOLOY
Using Wavelets Decomposition of Noisy signal Denoise of a signal is done in an iterative manner i.e., as number of iterations increases the better perfection can be seen. Equation (4) and equation (5) represents decomposition of signals using high pass filter for detailed coefficients and low pass filter for approximate coefficients which are down sampled by 2. Next approximate coefficients are further decomposed into approximate coefficients and detailed coefficients. This www.irjmets.com
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