Abstract
Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techniques in order to extract the desired speech signal from its corrupted speech signal. In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech. The basic principles of the wavelike transform are presented as an alternative to the Fourier transform. Or immediate switching of the window The practical results obtained are based on processing a large database dedicated to speech bookmarks polluted with various noises in many SNRs. This article tends to be an extension of practical research to improve speech signal for hearing aid purposes. Also learn about the main frequency of letters and their uses in intelligent systems, such as voice control systems.
Highlights
With the advent of wavelet analysis, it became popular to address unstable physical quantities, such as speech analysis, voice signature detection, and speech recognition
Wavelets have proven successful in front-end speech recognition processors that are an alternative to instant switching using time-wave resolution
A wide range of applications and usage has been found for these wavelets including signal processing, mathematics and numerical analysis and, for its better performance in signals and image processing it is considered an alternative to Fast Fourier Transform as DWT provide time frequency representation When there is a need for processing and analyzing non stationary tool, DWT can be used .Study shows that discrete wavelets transform have high performance in speech signal processing so far. [18-19]
Summary
A wide range of applications and usage has been found for these wavelets including signal processing, mathematics and numerical analysis and, for its better performance in signals and image processing it is considered an alternative to Fast Fourier Transform as DWT provide time frequency representation When there is a need for processing and analyzing non stationary tool, DWT can be used .Study shows that discrete wavelets transform have high performance in speech signal processing so far. Speech enhancement algorithms are created based on the application she block diagram of speech enhancement is show in figure (2) In this method, we rely on the processing of the audio signal stored in the database, where noise can be removed and the main frequencies of each letter can be identified. In The figure 7, the red represent the maximum spectral intensity, while the blue represents the minimum frequency
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