DocumentCode
2792809
Title
An effective pitch detection method for speech signals with low signal-to-noise ratio
Author
Zhao, Zhen-dong ; Hu, Xi-mei ; Tian, Jing-feng
Author_Institution
Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding
Volume
5
fYear
2008
fDate
12-15 July 2008
Firstpage
2775
Lastpage
2778
Abstract
Pitch detection in noisy environment plays an important role in speech analyzing and recognition. In this paper, an effective pitch detection method is proposed. Noised speech is denoised by an improved form of spectral subtraction method. A linear predictive coding analysis is performed on the segmented speech, and the segmented speech is filtered by the inverse filter to give the linear prediction error. The cepstrum of the linear prediction error and the autocorrelation function of the cepstrum are calculated. The result of the simulation shows that compared with the autocorrelation function pitch detection method, a distinct improvement in effect can be seen by using this improved method in pitch detection.
Keywords
cepstral analysis; correlation methods; linear predictive coding; signal denoising; signal detection; speech processing; autocorrelation function pitch detection method; cepstrum autocorrelation function; inverse filter; linear prediction error; linear predictive coding analysis; noised speech; noisy environment; segmented speech; signal denoising; signal-to-noise ratio; spectral subtraction method; speech analysis; speech recognition; speech signals; Autocorrelation; Cepstral analysis; Cepstrum; Linear predictive coding; Nonlinear filters; Signal to noise ratio; Speech analysis; Speech coding; Speech recognition; Working environment noise; Pitch detection; autocorrelation function; cepstrum; linear prediction error; spectral subtraction;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4244-2095-7
Electronic_ISBN
978-1-4244-2096-4
Type
conf
DOI
10.1109/ICMLC.2008.4620879
Filename
4620879
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