A fuzzy-wavelet denoising technique with applications to noise reduction in audio signals

In most of the data analysis tools, the sensitivity toward noise exists. Since real data is always contaminated by noise, a preprocessing technique to reduce noise is of interest. We propose a new method to eliminate noise using the fuzzy wavelet technique. We decompose a function using fuzzy wavelets to extract detail and approximation coefficients. Consequently, we threshold the detail coefficients to reduce the effect of noise and reconstruct a denoised signal. This new method exhibits robust behavior even applied for signals with a very small signal to noise ratio. It shows better results compared to ordinary wavelet denoising and fuzzy denoising on very irregular data. We apply the proposed method to noise reduction in audio signals and compare it with ordinary wavelet denoising. The obtained results are satisfactory.