Statistical Approach for Noise Removal in Speech Signals Using LMS, NLMS, Block LMS and RLS Adaptive filters

2012 ◽  
pp. 351-355
Author(s):  
D. Hari Hara Santosh ◽  
VUSL Sravya Pendyala ◽  
V. N. Lakshman Kumar ◽  
N. Shanmukh Rao
Keyword(s):  
Adaptive Filters ◽  
Statistical Approach ◽  
Noise Removal ◽  
Speech Signals

scholarly journals Sign Regressor based Normalized Adaptive Filters for Speech Enhancement Applications

2018 ◽  
Vol 7 (2.17) ◽  
pp. 79
Author(s):  
Jyoshna Girika ◽  
Md Zia Ur Rahman
Keyword(s):  
Steady State ◽  
Speech Enhancement ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Union Rate ◽  
Step Size ◽  
Adaptive Noise

Removal of noise components of speech signals in mobile applications  is an important step to facilitate high resolution signals to the user. Throughout the communication method the speech signals are tainted by numerous non stationary noises. The Least Mean Square (LMS) technique is a fundamental adaptive technique usedbroadly in numerouspurposes as anoutcome of its plainness as well as toughness. In LMS technique, an importantfactor is the step size. It bewell-known that if the union rate of the LMS technique will be rapidif the step size is speedy, but the steady-state mean square error (MSE) will raise. On the rival, for the diminutive step size, the steady state MSE will be minute, but the union rate will be conscious. Thus, the step size offers anexchange among the convergence rate and the steady-state MSE of the LMS technique. Build the step size variable before fixed to recover the act of the LMS technique, explicitly, prefer large step size values at the time of the earlyunion of the LMS technique, and usetiny step size values when the structure is near up to its steady state, which results in Normalized LMS (NLMS) algorithms. In this practice the step size is not stable and changes along with the fault signal at that time. The Less mathematical difficulty of the adaptive filter is extremely attractive in speech enhancement purposes. This drop usually accessible by extract either the input data or evaluation fault.  The algorithms depend on an extract of fault or data are Sign Regressor (SR) Algorithms. We merge these sign version to various adaptive noise cancellers. SR Weight NLMS (SRWNLMS), SR Error NLMS (SRENLMS), SR Unbiased LMS (SRUBLMS) algorithms are individual introduced as a quality factor. These Adaptive noise cancellers are compared with esteem to Signal to Noise Ratio Improvement (SNRI). 

Multi-layer adaptive filters trained with back propagation: A statistical approach

1994 ◽  
Vol 40 (1) ◽  
pp. 65-85 ◽  
Author(s):  
Mohamed Ibn Kahla ◽  
Zakariya Faraj ◽  
Francis Castanie ◽  
Jean Claude Hoffmann
Keyword(s):  
Adaptive Filters ◽  
Statistical Approach ◽  
Back Propagation

Noise Removal from EMG Signal Using Adaptive Enhanced Squirrel Search Algorithm

2020 ◽  
Vol 19 (04) ◽  
pp. 2050039
Author(s):  
B. Nagasirisha ◽  
V. V. K. D. V. Prasad
Keyword(s):  
Adaptive Filters ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Maximum Error ◽  
Noise Removal ◽  
Least Square ◽  
Mean Square ◽  
Least Mean Square ◽  
Recursive Least Square ◽  
Emg Signal

Electromyogram (EMG) signals are mostly affected by a large number of artifacts. Most commonly affecting artifacts are power line interference (PLW), baseline noise and ECG noise. This work focuses on a novel attenuation noise removal strategy which is concentrated on adaptive filtering concepts. In this paper, an enhanced squirrel search (ESS) algorithm is applied to remove noise using adaptive filters. The noise eliminating filters namely adaptive least mean square (LMS) filter and adaptive recursive least square (RLS) filters are designed, which is correlated with an ESS. This novel algorithm yields better performance than other existing algorithms. Here the performances are measured in terms of signal-to-noise ratio (SNR) in decibel, maximum error (ME), mean square error (MSE), standard deviation, simulation time and mean value difference. The proposed work has been implemented at the MATLAB simulation platform. Testing of their noise attenuation capability is also validated with different evolutionary algorithms namely squirrel search, particle swarm optimization (PSO), artificial bee colony (ABC), firefly, ant colony optimization (ACO) and cuckoo search (CS). The proposed work eliminates the noises and provides noise-free EMG signal at the output which is highly efficient when compared with existing methodologies. Our proposed work achieves 4%, 40%, 4%, 7%, 9% and 70% better performance than the literature mentioned in the results.

scholarly journals A nonlinear method of removing harmonic noise in geophysical data

2011 ◽  
Vol 18 (3) ◽  
pp. 367-379 ◽  
Author(s):  
Y. Jeng ◽  
C.-S. Chen
Keyword(s):  
Adaptive Filters ◽  
Signal Reconstruction ◽  
Adaptive Method ◽  
Noise Removal ◽  
Ensemble Empirical Mode Decomposition ◽  
Geophysical Data ◽  
Ocean Bottom ◽  
Nonlinear Method ◽  
Adaptive Data Analysis ◽  
Harmonic Noise

Abstract. A nonlinear, adaptive method to remove the harmonic noise that commonly resides in geophysical data is proposed in this study. This filtering method is based on the ensemble empirical mode decomposition algorithm in conjunction with the logarithmic transform. We present a synthetic model study to investigate the capability of signal reconstruction from the decomposed data, and compare the results with those derived from other 2-D adaptive filters. Applications to the real seismic data acquired by using an ocean bottom seismograph and to a shot gather of the ground penetrating radar demonstrate the robustness of this method. Our work proposes a concept that instead of Fourier-based approaches, the harmonic noise removal in geophysical data can be achieved effectively by using an alternative nonlinear adaptive data analysis method, which has been applied extensively in other scientific studies.

scholarly journals ECG signal denoising using a novel approach of adaptive filters for real-time processing

2021 ◽  
Vol 11 (2) ◽  
pp. 1243
Author(s):  
Amean Al-Safi
Keyword(s):  
Wavelet Transforms ◽  
Adaptive Filters ◽  
Reference Signal ◽  
Noise Removal ◽  
Signal Denoising ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Mean Square ◽  
Least Mean Square ◽  
Novel Approach

Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.

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