Hard component detection of transient noise and its removal using empirical mode decomposition and wavelet‐based predictive filter

Abstract

In this study, the authors propose a novel method that provides weapon sound (WS) interference reduction of military instruction sound or instructions sound (IS). This method comprises of the following steps. In the first step, the mixed signal is split into its basic constituents using principal component analysis. The second step provides the intrinsic mode functions using the empirical mode decomposition method. In the third step, the fundamental frequency component is extracted by cepstrum analysis. The localisation of WS is done using the grid search window-dominant signal subspace-based method in the fourth step. Multiscale prediction and filtering using Daubechies wavelet-based prediction are applied in the final step. The proposed method provides better results than existing baseline methods, for various signal-to-noise ratios. The simulations have also been performed on recorded WS corrupted IS signals from three different distances for the various input signal-to-weapon noise ratios, to verify the accuracy of the proposed method.

Rotor Bar Fault Diagnosis in Indirect Field–Oriented Control-Fed Induction Motor Drive Using Hilbert Transform, Discrete Wavelet Transform, and Energy Eigenvalue Computation

“…The EEV at each decomposition level is a useful diagnostic tool for assessing the severity of the fault, where the following equation is used to compute its value [43,44].…”

An Adaptive Multi-Homogeneous Sensor Weight Calculation Method for Body Sensor Networks

“…Previous researches have proposed many data revision methods, including polynomial fitting, wavelet decomposition, empirical mode decomposition and so on [3]- [7]. The missing information is also different for the same set of data after different revision processes.…”