Pregled bibliografske jedinice broj: 606247
Signal Denoising Using Wavelet Transformation with Variable Tresholding via Bayes Prediction and Ephraim-Malah Estimation
Signal Denoising Using Wavelet Transformation with Variable Tresholding via Bayes Prediction and Ephraim-Malah Estimation // 5th Congress of the Alps Adria Acoustics Association : an EAA Symposium : proceedings / Bucak, Tino ; Jambrošić, Kristian (ur.).
Zagreb: Hrvatsko akustičko društvo (HAD), 2012. str. SPS-03-1-SPS-03-6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 606247 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Signal Denoising Using Wavelet Transformation with Variable Tresholding via Bayes Prediction and Ephraim-Malah Estimation
Autori
Brajević, Zoran ; Petošić, Antonio ; Ivančević, Bojan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
5th Congress of the Alps Adria Acoustics Association : an EAA Symposium : proceedings
/ Bucak, Tino ; Jambrošić, Kristian - Zagreb : Hrvatsko akustičko društvo (HAD), 2012, SPS-03-1-SPS-03-6
ISBN
978-953-95097-1-0
Skup
5th Congress of the Alps Adria Acoustics Association : an EAA Symposium (5 ; 2012)
Mjesto i datum
Petrčane, Hrvatska, 12.09.2012. - 14.09.2012
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
noise disturbances; audio material; Discrete Wavelet Transform; Bayes prediction; Eprhraim-Malah estimation
Sažetak
This paper introduces a new audio cleaning method which is composed of combination of stochastically and wavelet transformation based approach. This method could be implemented on signals which have been inherently contaminated with some degree of stationary noise. This paper focuses also on ability of discrete wavelet transformation (DWT) coefficients to have excellent resolution and transparency of distinguishing between noise and useful part of signal. Beside DWT, this paper focuses on stochastically approach which we need to ensure the information about minimum mean-square error (MMSE) of the spectral amplitude estimator. This kind of estimation will be performed on a silence- or pause- interval via Bayes prediction method and Ephraim-Malah estimation. This procedure provide us the degree of thresholding the DWT coefficients. The introduced approach results in a significant reduction of noise and will be compared with other noise-cleaning methods. All audio results and experimentation presented in this paper are performed using audio signals sampled at the professional sampling rates of 44.1 kHz or 48 kHz and quantized to 16 bits.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
