In the actual transmission process, the signal will be disturbed by a lot of noise, which directly affects the quality of the signal. The purpose of speech enhancement technology is to denoise the signal containing noise, which is a method to improve the effectiveness of signal and remove noise. This paper is based on the wavelet transform threshold denoising method, in the wavelet transform wavelet denoising processing, research a new threshold selection algorithm, to achieve a better noise reduction effect, so as to achieve speech enhancement. Write a speech signal processing program in Matlab software, add other recorded speech signals to the program, process the signal with Gaussian noise, then replace the proposed algorithm into the speech signal processing program to denoise, and observe the signal waveform diagram reconstructed at last. It is compared with the time domain waveform of speech signal, soft threshold and hard threshold, and determines whether the new algorithm can achieve speech enhancement according to whether the signal denoising effect is improved.
Wavelet transform is a new time - frequency domain analysis tool after Fourier transform. Wavelet analysis has very important applications in image processing, including image compression, image denoising, image fusion, image decomposition and image enhancement and so on almost all stages of image processing. By comparing several edge detection methods and combining the multi-scale characteristics of wavelet transform, the approximate coefficient and high frequency coefficient of wavelet transform decomposition are processed, and an image edge detection algorithm based on wavelet transform is proposed. In the MATLAB environment, firstly, taking an image as an example, Roberts operator, Sobel operator, Prewitt operator and Canny operator are compared to verify that Canny operator has better performance in edge location. Then the db4 wavelet is applied to this image and the other two images, the image is decomposed and reconstructed, and the Canny operator is used for edge detection. The simulation results show that the edge details extracted by this method are more abundant.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.