Open AccessHard component detection of transient noise and its removal using empirical mode decomposition and wavelet‐based predictive filter
Author(s) -
Tanwar Pushpraj,
Somkuwar Ajay
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2017.0167
Subject(s) - computer science , hilbert–huang transform , noise (video) , wavelet , noise reduction , signal (programming language) , filter (signal processing) , interference (communication) , speech recognition , cepstrum , principal component analysis , algorithm , pattern recognition (psychology) , artificial intelligence , computer vision , telecommunications , channel (broadcasting) , image (mathematics) , programming language
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.