DocumentCode
3254691
Title
Improved priori SNR estimation for sound enhancement with Gaussian statistical model
Author
Xuemin Zhang ; Hang Jiang ; Jianhong Zhang
Author_Institution
Fac. of Electr. & Inf. Eng., Changchun Inst. of Technol., Changchun, China
fYear
2012
fDate
14-17 July 2012
Firstpage
1307
Lastpage
1310
Abstract
In allusion to the estimation problem of priori signal to noise ratio parameter in sound enhancement, by using Gaussian statistical model, a novel estimation algorithm for a priori signal to noise ratio was proposed in frequency domain. The presented algorithm with MMSE (Minimum Mean Square Error) computes directly the spectrum of the clean sound component to obtain the estimated priori signal to noise ratio of the decision directed approach, and thus the shortcoming of the two step noise reduction method is effectively eliminated. Moreover, this algorithm has good excellent performance in highly reducing the noise of the output sound while the advantages in noise suppression are retained. Comparing with the classic decision directed method and the recently proposed two step noise reduction technique, experimental results show that the proposed method in this paper has excellent performance under different noise background.
Keywords
Gaussian noise; frequency-domain analysis; least mean squares methods; speech enhancement; statistical analysis; Gaussian statistical model; MMSE; decision directed approach; frequency domain; minimum mean square error; noise ratio; noise ratio parameter; noise suppression; priori SNR estimation; sound enhancement; step noise reduction method; Estimation; Frequency domain analysis; Signal processing algorithms; Signal to noise ratio; Speech; Speech enhancement; Gaussian statistical model; MMSE; Priori SNR (Signal to Noise Ratio); Sound enhancement;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science & Education (ICCSE), 2012 7th International Conference on
Conference_Location
Melbourne, VIC
Print_ISBN
978-1-4673-0241-8
Type
conf
DOI
10.1109/ICCSE.2012.6295305
Filename
6295305
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