Denoising of one dimensional signal using threshold is one of the major applications of wavelet transform. Quadrature Mirror Filter bank method of wavelet transform has many advantages like support for all major orthogonal wavelets, dyadic resolution and adequate retention of energy. Determination of threshold type and threshold value is one of the important tasks in threshold based denoising techniques.
Denoising of audio signal is a subjective matter and remains a valid challenge. In this paper, a noisy speech wav file having additive white Gaussian noise is used for denoising to demonstrate features of two stage hard threshold, soft threshold and customized threshold denoising using Quadrature Mirror Filter bank method of wavelet transform. The second stage of denoising uses neighborhood concept where in a set of three wavelet coefficients, threshold is applied to any wavelet coefficient on the bases of the value of the other two neighborhood wavelet coefficients. Eight different denoised files are generated. Various parameters are measured and compared.