An Efficient Noise Reduction Technique by Using Spectral Subtraction and DWT

Wavelet transform is an important tool used in many application areas. This paper proposed the analysis of noise reduction techniques which are based on wavelet transform. In this paper three noise reduction techniques based on wavelet transform are described. These methods are Wavelet Split Coefficient; Hard Thresholding and Soft Thresholding. MATLAB GUI is developed for visualization of results which are obtained by using different techniques. With the help of these methods speech enhancement can be achieved. Due to this quality of speech can be improved with high efficiency. This wavelet transform can also be used to remove noise and blurring present in the image. In other words it is also effective in image processing to remove noise INTRODUCTION: Wavelet transform is most important tool which is used by many researchers to analyze the different types of signals. The wavelet transform provides the time-frequency representation of signal. Hence user can get information about the time and frequency simultaneously. Short-time Fourier transform (STFT) also provides information of both time and frequency but there are some limitations since it uses sliding window mechanism. The length of sliding window provides limitations on use of Short-time Fourier transform. But wavelet transform provides solution to this problem and hence nowadays it is widely used. Basically there are two basic types of wavelet transform. One type of wavelet transform is reversible that means original signal can be recovered back after it has been transformed. In second case there is no need to get back original signal that is original signal cannot be recovered after it has been transformed. There are many areas in which this wavelet transform is tremendously used. This transform is widely used in field of signal processing and image processing. In these two fields wavelet transform is used to remove the noise present in the signals and to remove blurring present in an image. Wavelet transform is also used in speech enhancement. Speech plays an important role in multimedia system. Hence it is very important to remove the noise present in speech signals and for this application wavelet transform is best tool. The wavelet transform has become a useful computational tool for a variety of signal and image processing applications proposes a Hue preserving algorithm, which uses a derived mapping function to modify the Saturation components, and CLAHE for Luminance components. TYPES OF SPEECH ENHANCEMENT: Speech enhancement methods are of different types. User needs to select appropriate speech enhancement techniques depending on application. The speech enhancement techniques can broadly classified based on number of channel used. So there are two different types of speech enhancement techniques as follows: a) Single Channel Speech Enhancement b) MultiChannel Speech Enhancement a) Single Channel Speech Enhancement: Single channel speech enhancement is particularly used where an alternate channel is not available for transmission of information from source to destination.