Discrete Cosine Wavelet Packet Transform and Its Application in Compressed Sensing for Speech Signal

Author(s):  
Changqing Zhang ◽  
Yanpu Chen ◽  
Wei Tan
Author(s):  
Montri Phothisonothai ◽  
◽  
Pinit Kumhom ◽  
Kosin Chamnongthai

Background noises interfere with communication devices such as mobile telephone, digital hearing aid, etc. Therefore noise reduction (NR) part for limiting the effect of these noises is important. The paper proposes a noise reduction method based on the soft decision-making by the fuzzy inference system (FIS). The different characteristics of noises frequently occurring are used for creating the fuzzy decision rule base of the FIS. The FIS have two input parameters: the average energy and the difference of the average energy. The analysis of the FIS is done in the domain of the perceptual wavelet packet transform (PWPT) that is the human’s psychoacoustic model. The output of the FIS is used to modify the PWPT coefficients in such a way that it is more likely that the noise components are reduced while the speech signal is enhanced. The enhanced speech signal is the result of the inverse perceptual wavelet packet transform (IPWPT) of the modified coefficients. The experiment results show that the proposed method gives lower distortion than do the conventional methods especially when the input signal-to-noise ratio (SNR) is low; e.g. at SRN at 0dB the proposed method improves the output SNR level up to 4.18dB.


2018 ◽  
Vol 44 (1) ◽  
pp. 36-39
Author(s):  
Mohammed Al-Turfi

This paper propose a method for security threw hiding the image inside the speech signal by replacing the high frequencycomponents of the speech signal with the data of the image where the high frequency speech components are separated and analyzed usingthe Wavelet Packet Transform (WPT) where the new signal will be remixed to create a new speech signal with an embedded image. The algorithm is implemented on MATLAB 15 and is designed to achieve best image hiding where the reconstruction rate was more than 94% while trying to maintain the same size of the speech signal to overcome the need for a powerful channel to handle the task. Best results were achieved with higher speech resolution (higher number of bits per sample) and longer periods (higher number of samples in the media file).


2017 ◽  
Vol 229 (3) ◽  
pp. 1275-1295 ◽  
Author(s):  
N. Jamia ◽  
P. Rajendran ◽  
S. El-Borgi ◽  
M. I. Friswell

2007 ◽  
Vol 46 (15) ◽  
pp. 5152-5158 ◽  
Author(s):  
J. Jay Liu ◽  
Daeyoun Kim ◽  
Chonghun Han

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