Novel Approach for Industrial Noise Cancellation in Speech Using ICA-EMD with PSO

Abstract

Speech Signals have high range of variation in amplitudes and frequency. These acoustic signals with diverse properties are hard to recognize and filter if mixed with noise. To separate noise from original signal, the artifact peaks are separated from original signal and discarded. In this paper, the ICA method of signal denoising is used to differentiate the speech signal from periodic noise and Empirical Mode Decomposition method is proposed to generate the components of signal. The IMF(s) of signal is the non-linear descending order of frequency components that have been filtered for better SNR. Filtering with wiener filter has amended output but also results in loss of information. The selection of IMF(s) for signal regeneration when optimized using objective function of PSO, the information of original signal was dramatically preserved with suppressed noise. The system is tested on 4 example signals and proposed technique illustrates lower mean square error and higher SNR compared to wiener and ICA.

Verbal Communication: Noise Reduction and Characteristics Analysis Using Wavelet and Threshold Derivative Method

“…For affixed time frequency resolution short time fourier transform is used and for multi resolution technique, wavelet transform is used. [4] Wavelet transform analysis can perform local analysis which serves as a great advantage. Wavelet analysis can express the signal appearance such as breakdown points, discontinuities etc., that other analysis techniques cannot express.…”

A Novel Denoising Method for an Acoustic-Based System through Empirical Mode Decomposition and an Improved Fruit Fly Optimization Algorithm

“…Soft threshold function was applied and the denoised signal was reconstructed as Equation (9). Some parameters during the EMD-PSO were set as recommended in [23]: M1 = 2, M2 = 4, the dimensionality of (3) EMD-PSO denoising. The noisy signal was first decomposed by EMD adaptively.…”

“…Soft threshold function was applied and the denoised signal was reconstructed as Equation (9). Some parameters during the EMD-PSO were set as recommended in [23]: M 1 = 2, M 2 = 4, the dimensionality of each particle was M 2 − M 1 + 1 = 3, the learning factor c 1 and c 2 were set as 2, the iteration number was 100 and the particle number of the population was 10. (4) EMD-FOA denoising.…”

“…The EMD denoising scheme can be regarded as partially reconstructing the signal, retaining only the IMFs that include useful information and discarding those contain primarily noise. To cancel industrial noise from speech signal, EMD was applied to generate the components of signal and the selection of IMFs for signal regeneration is optimized by using objective function of the intelligent particle swarm optimization algorithm (PSO) [23]. Then, the EMD denoising procedure inspired by wavelet threshold was proposed and achieved some preliminary outcomes.…”