Thresholding Wavelet Networks for Signal Classification

Thresholding Wavelet Networks for Signal Classification

This paper reports a new signal classification tool, a modified wavelet network called Thresholding Wavelet Networks (TWN). The network is designed for the purposes of classifying signals. The philosophy of the technique is that often the difference between signals may not lie in the spectral or temporal region where the signal strength is high. Unlike other wavelet networks, this network does ...

Signal and Image Denoising via Wavelet Thresholding

{ In this paper we discuss wavelet thresholding in the context of scalar orthogonal, scalar biorthogonal, multiple orthogonal and multiple biorthogonal wavelet transforms. Two types of multiwavelet thresholding are considered: scalar and vector. Both of them take into account the covariance structure of the transform. The form of the universal threshold is carefully formulated. The results of n...

Speech signal enhancement through adaptive wavelet thresholding

This paper demonstrates the application of the Bionic Wavelet Transform (BWT), an adaptive wavelet transform derived from a nonlinear auditory model of the cochlea, to the task of speech signal enhancement. Results, measured objectively by Signal-to-Noise ratio (SNR) and Segmental SNR (SSNR) and subjectively by Mean Opinion Score (MOS), are given for additive white Gaussian noise as well as fou...

Radar signal detection using wavelet thresholding

Abstract The purpose of the research is to study the effect of wavelet-based denoising (WBD) technique in radar signal processing. In this paper, Block thresholding is used for WBD and its performance is compared with general MTI filter by simulation and experimental results. The comparisons which are based upon ROC and visual inspection of the resulting signals are accomplished for different D...

Classification , Multiple Hypothesis Testing and Wavelet Thresholding Procedures with Applications