Abstract : In this work we present a filtering selection approach for efficient ANC system. Active noise cancellation (ANC) has wide application in next generation human machine interaction to automobile Heating Ventilating and Air Conditioning (HVAC) devices. We compare conventional adaptive filters algorithms LMS, NLMS, VSLMS, VSNLMS, VSLSMS for a predefined input sound file, where various algorithms run and result in standard output and better performance. The wiener filter based on least means squared (LMS) algorithm family is most sought after solution of ANC. This family includes LMS, NLMS, VSLMS, VSNLMS, VFXLMS, FX-sLMS and many more. Some of these are nonlinear algorithm, which provides better solution for nonlinear noisy environment. The components of the ANC systems like microphones and loudspeaker exhibit nonlinearities themselves. The nonlinear transfer function create worse situation. This is a task which is some sort of a prediction of suitable solution to the problems. The Radial Basis Function of Neural Networks (RBF NN) has been known to be suitable for nonlinear function approximation [1]. The classical approach to RBF implementation is to fix the number of hidden neurons based on some property of the input data, and estimate the weights connecting the hidden and output neurons using linear least square method. So an efficient novel decisive approach for better performing ANC algorithms has been proposed. Keywords - Adaptive filters, Winner filter ANC, Least mean square, N LMS, VSNLMS, RBF.
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713CSCJournals
In noisy acoustic environment, audio signal in speech communication from mobile phone, moving car, train, aero plane, or over a noisy telephone channel is corrupted by additive random noise. The noise is unwanted signal and it is desirable to remove noise from original signal. Since noise is random process and varying at every instant of time, we need to estimate noise at every instant to remove it from original signal. There are many schemes for noise removal but most effective scheme to accomplish noise cancellation is to use adaptive filters. In this paper, we have carried out simulations for different adaptive algorithms (LMS, NLMS and RLS) and compared their performance for noise cancellation in noisy environment. Real time implementation of adaptive algorithm over DSP kit (TMS320C6713) is also presented in this paper. Performance of adaptive algorithm over hardware is also presented. Developed system incorporating best performance adaptive filter in any noisy environment can be used for noise cancellation.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
Comparison of different Sub-Band Adaptive Noise Canceller with LMS and RLSijsrd.com
Sub-band adaptive noise is employed in various fields like noise cancellation, echo cancellation and system identification etc. It reduces computational complexity and improve convergence rate. In this paper we perform different Sub-band noise cancellation method for simulation. The Comparison with different algorithm has been done to find out which one is best.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Hardware Implementation of Adaptive Noise Cancellation over DSP Kit TMS320C6713CSCJournals
In noisy acoustic environment, audio signal in speech communication from mobile phone, moving car, train, aero plane, or over a noisy telephone channel is corrupted by additive random noise. The noise is unwanted signal and it is desirable to remove noise from original signal. Since noise is random process and varying at every instant of time, we need to estimate noise at every instant to remove it from original signal. There are many schemes for noise removal but most effective scheme to accomplish noise cancellation is to use adaptive filters. In this paper, we have carried out simulations for different adaptive algorithms (LMS, NLMS and RLS) and compared their performance for noise cancellation in noisy environment. Real time implementation of adaptive algorithm over DSP kit (TMS320C6713) is also presented in this paper. Performance of adaptive algorithm over hardware is also presented. Developed system incorporating best performance adaptive filter in any noisy environment can be used for noise cancellation.
Echo Cancellation Algorithms using Adaptive Filters: A Comparative Studyidescitation
An adaptive filter is a filter that self-adjusts its transfer function according to an
optimization algorithm driven by an error signal. Adaptive filter finds its essence in
applications such as echo cancellation, noise cancellation, system identification and many
others. This paper briefly discusses LMS, NLMS and RLS adaptive filter algorithms for
echo cancellation. For the analysis, an acoustic echo canceller is built using LMS, NLMS
and RLS algorithms and the echo cancelled samples are studied using Spectrogram. The
analysis is further extended with its cross-correlation and ERLE (Echo Return Loss
Enhancement) results. Finally, this paper concludes with a better adaptive filter algorithm
for Echo cancellation. The implementation and analysis is done using MATLAB®,
SIMULINK® and SPECTROGRAM V5.0®.
Comparison of different Sub-Band Adaptive Noise Canceller with LMS and RLSijsrd.com
Sub-band adaptive noise is employed in various fields like noise cancellation, echo cancellation and system identification etc. It reduces computational complexity and improve convergence rate. In this paper we perform different Sub-band noise cancellation method for simulation. The Comparison with different algorithm has been done to find out which one is best.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
Noise is the most serious issue in the filters and adaptive filters are subjected to this unwanted
component. This paper deals with the problem of the adaptive noise and various adaptive algorithms functions
which when implemented practically shows that the noise is cancelled or removed by the neural network
approach using the exact random basis function. The adaptive filters are used to control the noise and it has a
linear input and output characteristics. This approach is done so as to get the minimum possible error so that to
obtain the error free desired signal. The designed filter will reduce this noise from measured signal by a
reference signal which is highly correlated with the noise signal. This approach gives excellent result for this
signal processing technique that removes or eliminates the linear noise from the different functions. The
simulation results are also mentioned so as to gives a vivid idea of reduced noise using neural networks
algorithm.
Acoustic problems in an environment has gained more attention due to the tremendous growth of technology that lead to noisy engines, heavy machineries, pumps, air condition, music and other noise sources. Normally human ears are very sensitive at audio range (lower frequency) from 20 Hz to 20 kHz. So, any sound within these frequencies has the tendency to disturb human hearing and can be classified as noise. The reduction of acoustic noise in speech has been investigated for many Years .The major application of noise reduction is by improving voice communication and eliminating the noise using adaptive noise canceler.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
We are trying to implement an adaptive filter with input weights. The adaptive parameters are obtained by simulating noise canceller on MATLAB. Simulink model of adaptive Noise canceller was developed and Processed by FPGA.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas
such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal
processing applications. The key to successful adaptive signal processing understands the fundamental
properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the
noise component which is overlap with undesired signal in the same frequency range. This paper presents
design, implementation and performance comparison of adaptive FIR filter using LMS and RMS
algorithms. MATLAB Simulink environment are used for simulations
Simulation of Adaptive Noise Canceller for an ECG signal AnalysisIDES Editor
In numerous applications of signal processing,
communications and biomedical we are faced with the
necessity to remove noise and distortion from the signals.
Adaptive filtering is one of the most important areas in digital
signal processing to remove background noise and distortion.
In last few years various adaptive algorithms are developed
for noise cancellation. In this paper we have presented an
implementation of LMS (Least Mean Square), NLMS
(Normalized Least Mean Square) and RLS (Recursive Least
Square) algorithms on MATLAB platform with the intention
to compare their performance in noise cancellation application.
We simulate the adaptive filter in MATLAB with a noisy ECG
signal and analyze the performance of algorithms in terms of
MSE (Mean Squared Error), SNR Improvement,
computational complexity and stability. The obtained results
shows that, the RLS algorithm eliminates more noise from
noisy ECG signal and has the best performance but at the cost
of large computational complexity and higher memory
requirements.
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMSEditor IJMTER
This paper describes the concept of adaptive noise cancelling. The noise cancellation
using the Recursive Least Squares (RLS) to remove the noise from an input signal. The RLS adaptive
filter uses the reference signal on the Input port and the desired signal on the desired port to
automatically match the filter response in the Noise Filter block. The filtered noise should be completely
subtracted from the "noisy signal” of the input Sine wave & noise input signal, and the "Error Signal"
should contain only the original signal. Finally, the functions of field programmable gate array based
system structure for adaptive noise canceller based on RLS algorithm are synthesized, simulated, and
implemented on Xilinx XC3s200 field programmable gate array using Xilinx ISE tool.
echo types, how to cancel echo in each type, which is more complex, echo cancellation implementation in matlab
prepared by : OLA MASHAQI ,, SUHAD MALAYSHE
Performance analysis of adaptive noise canceller for an ecg signalRaj Kumar Thenua
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
In this paper, a simple and efficient filtered- X
Least Mean Square (FXLMS) algorithm is used for the
removal of different kinds of noises from the ECG signal. The
adaptive filter essentially minimizes the mean-squared error
between a primary input, which is the noisy ECG, and a
reference input, which is either noise that is correlated in
some way with the noise in the primary input or a signal that
is correlated only with ECG in the primary input. Different
filter structures are presented to eliminate the diverse forms
of noise: baseline wander, 60 Hz power line interference,
muscle artifacts and motion artifacts. Finally different
adaptive structures are implemented to remove artifacts from
ECG signals and tested on real signals obtained from MITBIH
data base. Simulation studies shows that the proposed
realization gives better performance compared to existing
realizations in terms of signal to noise ratio.
The determination of complex underlying relationships between system parameters from simulated and/or recorded data requires advanced interpolating functions, also known as surrogates. The development of surrogates for such complex relationships often requires the modeling of high dimensional and non-smooth functions using limited information. To this end, the hybrid surrogate modeling paradigm, where different surrogate models are aggregated, offers a robust solution. In this paper, we develop a new high fidelity surro- gate modeling technique that we call the Reliability Based Hybrid Functions (RBHF). The RBHF formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adap- tively combines the favorable characteristics of different surrogate models. The weight of each contributing surrogate model is determined based on the local reliability measure for that surrogate model in the pertinent trust region. Such an approach is intended to ex- ploit the advantages of each component surrogate. This approach seeks to simultaneously capture the global trend of the function and the local deviations. In this paper, the RBHF integrates four component surrogate models: (i) the Quadratic Response Surface Model (QRSM), (ii) the Radial Basis Functions (RBF), (iii) the Extended Radial Basis Functions (E-RBF), and (iv) the Kriging model. The RBHF is applied to standard test problems. Subsequent evaluations of the Root Mean Squared Error (RMSE) and the Maximum Ab- solute Error (MAE), illustrate the promising potential of this hybrid surrogate modeling approach.
Presentation of my NSERC-USRA funded summer research project given at the Canadian Undergraduate Mathematics Conference (CUMC) 2014.
Please refer to the project site: http://jessebett.com/Radial-Basis-Function-USRA/
Acoustic problems in an environment has gained more attention due to the tremendous growth of technology that lead to noisy engines, heavy machineries, pumps, air condition, music and other noise sources. Normally human ears are very sensitive at audio range (lower frequency) from 20 Hz to 20 kHz. So, any sound within these frequencies has the tendency to disturb human hearing and can be classified as noise. The reduction of acoustic noise in speech has been investigated for many Years .The major application of noise reduction is by improving voice communication and eliminating the noise using adaptive noise canceler.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
We are trying to implement an adaptive filter with input weights. The adaptive parameters are obtained by simulating noise canceller on MATLAB. Simulink model of adaptive Noise canceller was developed and Processed by FPGA.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
Interest in adaptive filters continues to grow as they begin to find practical real-time applications in areas
such as channel equalization, echo cancellation, noise cancellation and many other adaptive signal
processing applications. The key to successful adaptive signal processing understands the fundamental
properties of adaptive algorithms such as LMS, RLS etc. Adaptive filter is used for the cancellation of the
noise component which is overlap with undesired signal in the same frequency range. This paper presents
design, implementation and performance comparison of adaptive FIR filter using LMS and RMS
algorithms. MATLAB Simulink environment are used for simulations
Simulation of Adaptive Noise Canceller for an ECG signal AnalysisIDES Editor
In numerous applications of signal processing,
communications and biomedical we are faced with the
necessity to remove noise and distortion from the signals.
Adaptive filtering is one of the most important areas in digital
signal processing to remove background noise and distortion.
In last few years various adaptive algorithms are developed
for noise cancellation. In this paper we have presented an
implementation of LMS (Least Mean Square), NLMS
(Normalized Least Mean Square) and RLS (Recursive Least
Square) algorithms on MATLAB platform with the intention
to compare their performance in noise cancellation application.
We simulate the adaptive filter in MATLAB with a noisy ECG
signal and analyze the performance of algorithms in terms of
MSE (Mean Squared Error), SNR Improvement,
computational complexity and stability. The obtained results
shows that, the RLS algorithm eliminates more noise from
noisy ECG signal and has the best performance but at the cost
of large computational complexity and higher memory
requirements.
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMSEditor IJMTER
This paper describes the concept of adaptive noise cancelling. The noise cancellation
using the Recursive Least Squares (RLS) to remove the noise from an input signal. The RLS adaptive
filter uses the reference signal on the Input port and the desired signal on the desired port to
automatically match the filter response in the Noise Filter block. The filtered noise should be completely
subtracted from the "noisy signal” of the input Sine wave & noise input signal, and the "Error Signal"
should contain only the original signal. Finally, the functions of field programmable gate array based
system structure for adaptive noise canceller based on RLS algorithm are synthesized, simulated, and
implemented on Xilinx XC3s200 field programmable gate array using Xilinx ISE tool.
echo types, how to cancel echo in each type, which is more complex, echo cancellation implementation in matlab
prepared by : OLA MASHAQI ,, SUHAD MALAYSHE
Performance analysis of adaptive noise canceller for an ecg signalRaj Kumar Thenua
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
In this paper, a simple and efficient filtered- X
Least Mean Square (FXLMS) algorithm is used for the
removal of different kinds of noises from the ECG signal. The
adaptive filter essentially minimizes the mean-squared error
between a primary input, which is the noisy ECG, and a
reference input, which is either noise that is correlated in
some way with the noise in the primary input or a signal that
is correlated only with ECG in the primary input. Different
filter structures are presented to eliminate the diverse forms
of noise: baseline wander, 60 Hz power line interference,
muscle artifacts and motion artifacts. Finally different
adaptive structures are implemented to remove artifacts from
ECG signals and tested on real signals obtained from MITBIH
data base. Simulation studies shows that the proposed
realization gives better performance compared to existing
realizations in terms of signal to noise ratio.
The determination of complex underlying relationships between system parameters from simulated and/or recorded data requires advanced interpolating functions, also known as surrogates. The development of surrogates for such complex relationships often requires the modeling of high dimensional and non-smooth functions using limited information. To this end, the hybrid surrogate modeling paradigm, where different surrogate models are aggregated, offers a robust solution. In this paper, we develop a new high fidelity surro- gate modeling technique that we call the Reliability Based Hybrid Functions (RBHF). The RBHF formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adap- tively combines the favorable characteristics of different surrogate models. The weight of each contributing surrogate model is determined based on the local reliability measure for that surrogate model in the pertinent trust region. Such an approach is intended to ex- ploit the advantages of each component surrogate. This approach seeks to simultaneously capture the global trend of the function and the local deviations. In this paper, the RBHF integrates four component surrogate models: (i) the Quadratic Response Surface Model (QRSM), (ii) the Radial Basis Functions (RBF), (iii) the Extended Radial Basis Functions (E-RBF), and (iv) the Kriging model. The RBHF is applied to standard test problems. Subsequent evaluations of the Root Mean Squared Error (RMSE) and the Maximum Ab- solute Error (MAE), illustrate the promising potential of this hybrid surrogate modeling approach.
Presentation of my NSERC-USRA funded summer research project given at the Canadian Undergraduate Mathematics Conference (CUMC) 2014.
Please refer to the project site: http://jessebett.com/Radial-Basis-Function-USRA/
Evaluation of Wash and Light Fastness of Some Selected Printed FabricsIOSR Journals
Abstract: The printed fabrics were subjected to ISO2, ISO3, and ISO4 wash fastness test and assessed for
change in colour and staining using the grey scale. The change in colour of the tested specimen and the staining
of the adjacent undyed cloths were assessed with the appropriate grey scales. The fabrics were also tested for
light fastness property. The specimen and the blue standard were exposed behind a glass and inserted into the
light fastness testing machine. Exposure was carried out for 48 hours. Based on the research carried out, it was
found that the selected foreign fabrics show a higher wash and light fastness property as compared to the local
fabrics which also show high wash and light fastness property.
Restoration of Images Corrupted by High Density Salt & Pepper Noise through A...IOSR Journals
Abstract: In this paper an efficient algorithm is proposed for removal of salt & pepper noise from digital images. Salt and pepper noise in images is present due to bit errors in transmission or introduced during the signal acquisition stage. It represents itself as randomly occurring white and black pixels. This noise can be removed using standard Median Filter (SMF), Progressive Switched Median Filter (PSMF) under low density noise conditions. Decision Based Algorithm (DBA) and Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) do not give better results at high noise density. So, in this project, this drawback will be overcome by using Adaptive Median based Modified Mean Filter (AMMF). This proposed algorithm shows better Peak Signal-to-Noise Ratio and clear image than the existing algorithm. Keywords- Median filter, Progressive Switched Median Filter, Decision Based Algorithm, Modified Decision Based Unsymmetric Trimmed Median Filter
P ERFORMANCE A NALYSIS O F A DAPTIVE N OISE C ANCELLER E MPLOYING N LMS A LG...ijwmn
n voice communication systems, noise cancellation
using adaptive digital filter is a renowned techniq
ue
for extracting desired speech signal through elimin
ating noise from the speech signal corrupted by noi
se.
In this paper, the performance of adaptive noise ca
nceller of Finite Impulse Response (FIR) type has b
een
analysed employing NLMS (Normalized Least Mean Squa
re) algorithm.
An extensive study has been made
to investigate the effects of different parameters,
such as number of filter coefficients, number of s
amples,
step size, and input noise level, on the performanc
e of the adaptive noise cancelling system. All the
results
have been obtained using computer simulations built
on MATLAB platform.
Comparison of Stable NLMF and NLMS Algorithms for Adaptive Noise Cancellation...IJERA Editor
The least mean fourth (LMF) algorithm has several stability problems. Its stability depends on the variance and distribution type of the adaptive filter input, the noise variance, and the initialization of filter weights. A global solution to these stability problems was presented recently for a normalized LMF (NLMF) algorithm. The analysis is done in context of adaptive noise cancellation with Gaussian, binary, and uniform desired signals. The analytical model is shown to accurately predict the optimum solutions. Comparisons of the NLMF and NLMS algorithms are then made for various parameter selections. It is then shown under what conditions the NLMF algorithm is superior to NLMS algorithm for adaptive noise cancelling.
Design and Implementation of Polyphase based Subband Adaptive Structure for N...Pratik Ghotkar
With the tremendous growth in the Digital Signal processing technology, there are many techniques available to remove noise from the speech signals which is used in the speech processing. Widely used LMS algorithm is modified with much advancement but still there are many limitations are introducing. This paper consist of a new approach i.e. subband adaptive processing for noise cancelation in the speech signals. Subband processing employs the multirate signal processing. The polyphase based subband adaptive implementation finds better results in term of MMSE , PSNR and processing time; also the synthesis filter bank is works on the lower data rate which reduces the computational Burdon as compare to the direct implementation of Subband adaptive filter. The normalized least mean squares (NLMS) algorithm is a class of adaptive filter used.
Comparison of fx lms and n fxlms algorithms in matlab using active vibration ...IJARIIT
The paper presents simulation results of the performance of adaptive filtering algorithms such as Filtered –x Least
Mean Square (FxLMS) and the Normalized Filtered-x Least Mean Square (NFxLMS) algorithm using the concept of active
vibration control. The FxLMS and NFxLMS algorithms are most popular in adaptive filtering feed-forward control methods.
The FxLMS and the NFxLMS are used in order to overcome the disadvantages of conventional Least Mean Square (LMS)
algorithm. The MATLAB implementations for both the algorithms are carried out and the parameters such as convergence rate,
efficiency, and step size results are compared using the principle of Active Vibration Control.
During data acquisition and transmission of biomedical signals like electrocardiography (ECG), different types of artifacts are embedded in the signal. Since an ECG is a low amplitude signal these artifacts greatly degrade the signal quality and the signal becomes noisy. The sources of artifacts are power line interference (PLI), high frequency interference electromyography (EMG) and base line wanders (BLW). Different digital filters are used in order to reduce these artifacts. ECG signal is a non-stationary signal, it is difficult to find fixed filters for the removal of interference from the ECG signal. In order to overcome these problems adaptive filters are used as they are well suited for the non-stationary environment. In this paper a new algorithm “Modified Normalized Least Mean Square” has been proposed. A comparison is made among the new algorithm and the existing algorithms like LMS, NLMS, Sign data LMS and Log LMS in terms of SNR, convergence rate and time complexity. It has been observed that the performance of new algorithm is superior to the existing ones in terms of SNR and convergence rate however it is more complex than the other algorithms. Results of simulations in MATLAB are presented and a critical analysis is made on the basis of convergence rate, signal to noise ratio (SNR), and computational time among the filtering techniques.
Performance Variation of LMS And Its Different VariantsCSCJournals
Acoustic echo cancellation is an essential and important requirement for various applications such as, telecasting, hands-free telephony and video-conferencing. Echo cancellers are required because of loud-speaker signals are picked up by a microphone and are fed back to the correspondent, resulting in an undesired echo. These days, adaptive filtering methods are used to cancel the affect of these echoes. Different variants of LMS adaptive algorithms have been. Implemented and they are compared based upon their performance according to the choice of step size
Suppression of noise in noisy speech signal is required in many speech enhancement applications like signal recording and transmission from one place to other. In this paper a novel single line noise cancellation system is proposed using derivative of normalized least mean spare algorithm. The proposed system has two phases. The first phase is generation of secondary reference signal from incoming primary signal itself at initial silence period and pause between two words, which is essential while adaptive filter using as noise canceller. Second phase is noise cancellation using proposed modified error data normalized step size (EDNSS) algorithm. The performance of the proposed algorithm is compared with normalized least mean square (NLMS) algorithm and original EDNSS algorithm using standard IEEE sentence (SP23) of Noizeus data base with different types of real-world noise at different level of signal to noise ratio (SNR). The output of proposed, NLMS and EDNSS algorithm are measured with output SNR, excessive mean square error (EMSE) and misadjustment (M). The results clearly illustrates that the proposed algorithm gives improved result over conventional NLMS and EDNSS algorithm. The speed of convergence is also maintained as same conventional NLMS algorithm.
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
We have proposed new approach for the speech recognition system by applying kernel adaptive filter for
speech enhancement and for the recognition, the hybrid HMM/DTW methods are used in this paper. Noise
removal is very important in many applications like telephone conversation, speech recognition, etc. In the
recent past, the kernel methods are showing good results for speech processing applications. The feature
used in the recognition process is MFCC features. It consists of a HMM system used to train the speech
features and for classification purpose used the DTW method. Experimental results show a relative
improvement of recognition rate compared to the traditional methods.
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Performance Analysis of Acoustic Echo Cancellation TechniquesIJERA Editor
Mainly, the adaptive filters are implemented in time domain which works efficiently in most of the applications. But in many applications the impulse response becomes too large, which increases the complexity of the adaptive filter beyond a level where it can no longer be implemented efficiently in time domain. An example of where this can happen would be acoustic echo cancellation (AEC) applications. So, there exists an alternative solution i.e. to implement the filters in frequency domain. AEC has so many applications in wide variety of problems in industrial operations, manufacturing and consumer products. Here in this paper, a comparative analysis of different acoustic echo cancellation techniques i.e. Frequency domain adaptive filter (FDAF), Least mean square (LMS), Normalized least mean square (NLMS) &Sign error (SE) is presented. The results are compared with different values of step sizes and the performance of these techniques is measured in terms of Error rate loss enhancement (ERLE), Mean square error (MSE)& Peak signal to noise ratio (PSNR).
Capacity Enhancement of MIMO-OFDM System in Rayleigh Fading ChannelIOSR Journals
MIMO-OFDM system in Rayleigh Fading Channel is very popular technique for mobile
communication now a day’s for research. Here we want increase the capacity of MIMO-OFDM of system by
using adaptive modulation, Algebraic Space-Time Codes (ASTC) encoder for MIMO Systems are based on
quaternion algebras .we found that ergodic capacity has some limitation which reduce the system’s
performance to overcome this we use ASTC code . ASTC code are full rank, full rate and non vanishing constant
minimum determinant for increasing spectral efficiency and reducing Peak to Average Power Ratio (PAPR) .
Capacity Enhancement of MIMO-OFDM System in Rayleigh Fading ChannelIOSR Journals
Abstract: MIMO-OFDM system in Rayleigh Fading Channel is very popular technique for mobile communication now a day’s for research. Here we want increase the capacity of MIMO-OFDM of system by using adaptive modulation, Algebraic Space-Time Codes (ASTC) encoder for MIMO Systems are based on quaternion algebras .we found that ergodic capacity has some limitation which reduce the system’s performance to overcome this we use ASTC code . ASTC code are full rank, full rate and non vanishing constant minimum determinant for increasing spectral efficiency and reducing Peak to Average Power Ratio (PAPR) . Keywords— Adaptive modulation ASTC code, Capacity, BER, Ergodic capacity, PAPR, Spectral Efficiency and SNR
BER Analysis ofImpulse Noise inOFDM System Using LMS,NLMS&RLSiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Performance analysis of adaptive beamforming at receiver side by using lms an...Ijrdt Journal
The Least Mean Squares (LMS) algorithm is an important member of the family of stochastic gradient algorithms. A significant feature of the LMS algorithm is its simplicity. The recursive least squares (RLS) algorithm recursively finds the filter coefficients for minimizing linear least squares cost function. Smart antenna generally refers to any antenna array. Beamforming is a signal processing technique used in sensor arrays for directional signal transmission or reception. This spatial selectivity is achieved by using adaptive or fixed receive/transmit beam patterns. The improvement compared with an omnidirectional reception/transmission is known as the receive/transmit gain (or loss). In this study, fixed weight beamforming basics and maximum signal to interference ratio are given. The theoretical information of adaptive beamforming, LMS (Least Mean Square) and RLS (Recursive Mean Squares) algorithms are explained. Adaptive beamforming in recieve antenna is simulated by using LMS and RLS algorithms. Simulation results are discussed and explained.
Similar to A Decisive Filtering Selection Approach For Improved Performance Active Noise Cancellation System (20)
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Planning Of Procurement o different goods and services
A Decisive Filtering Selection Approach For Improved Performance Active Noise Cancellation System
1. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)
e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume 7, Issue 4 (Sep. - Oct. 2013), PP 83-87
www.iosrjournals.org
www.iosrjournals.org 83 | Page
A Decisive Filtering Selection Approach For Improved
Performance Active Noise Cancellation System
Abha Nyati1
, S K Dargar2
, **Deepak Vyas
1
(Department of E & C Engineering, Pacific University, (PAHER) Udaipur, Rajasthan, India)
2
(Faculty, Department of E & C Engineering, SPSU University, Udaipur, Rajasthan, India)
**
(Faculty, Department of E & C Engineering, Pacific Institute of Technology, Udaipur, Rajasthan, India)
Abstract : In this work we present a filtering selection approach for efficient ANC system. Active noise
cancellation (ANC) has wide application in next generation human machine interaction to automobile Heating
Ventilating and Air Conditioning (HVAC) devices. We compare conventional adaptive filters algorithms LMS,
NLMS, VSLMS, VSNLMS, VSLSMS for a predefined input sound file, where various algorithms run and result
in standard output and better performance. The wiener filter based on least means squared (LMS) algorithm
family is most sought after solution of ANC. This family includes LMS, NLMS, VSLMS, VSNLMS, VFXLMS, FX-
sLMS and many more. Some of these are nonlinear algorithm, which provides better solution for nonlinear
noisy environment. The components of the ANC systems like microphones and loudspeaker exhibit
nonlinearities themselves. The nonlinear transfer function create worse situation. This is a task which is some
sort of a prediction of suitable solution to the problems. The Radial Basis Function of Neural Networks (RBF
NN) has been known to be suitable for nonlinear function approximation [1]. The classical approach to RBF
implementation is to fix the number of hidden neurons based on some property of the input data, and estimate
the weights connecting the hidden and output neurons using linear least square method. So an efficient novel
decisive approach for better performing ANC algorithms has been proposed.
Keywords - Adaptive filters, Winner filter ANC, Least mean square, N LMS, VSNLMS, RBF.
I. OVERVIEW
Acoustic Noise Cancellation is a method for reducing undesired noise. It is achieved by introducing a
canceling “anti-noise” wave through secondary sources. These secondary sources are interconnected through an
electronic system using a specific signal processing algorithm for the particular cancellation scheme. Noise
cancellation makes use of the notion of destructive interference. When two sinusoidal waves are superimposed,
the resultant waveform depends on the frequency amplitude and relative phase of the two waves. If the original
wave and the inverse of the original wave encounter at a junction at the same time, total cancellation occurs. The
challenges are to identify the original signal and generate the inverse noise cancellation on inverse wave without
delay in all directions where noises interact and superimpose. We meet in our everyday life. Echo phenomena
are interesting and entertaining, but their presences in communication networks are undesirable and represent a
serious problem. Echo is delayed and degraded version of original signal which travels back to its source after
several reflections or because of some other reason. Nature of echo signal can be either acoustic or electrical,
and in order to reduce its undesired effect we employ echo cancellers. Design of echo cancellers requires an
application of adaptive filter theory. Echo cancellers must work within specific time limits so adaptive
algorithms must provide fast convergence of filter parameters. We‟ve been applying echo cancellers
successfully for many years, but we always tend to improve them and increase their efficiency.
The wiener filter based least means squared (LMS) algorithm family is most sought after solution of
ANC. This family includes LMS , Fx-LMS, VFx-LMS, FsLMS and many more . Then there are Kalman filter
algorithms which are basically based on recursive least square algorithm. Some of these are nonlinear algorithm,
which provides better solution for non linear noisy environment. The components of the ANC systems like
microphones and loudspeaker exhibit nonlinearities themselves. The non linear transfer function of primary and
secondary path may itself create worse situation.
II. BACKGROUND LITERATURE REVIEW
Active Noise Control (ANC) includes an electromechanical system or electro acoustic system that
cancels the unwanted noise based on the principle of superposition of an anti-noise wave of equal amplitude.
The customary ways for active noise cancellers uses passive techniques such as enclosures, barriers, and
silencers to attenuate the undesired noise. These passive silencers are valued for their high attenuation over a
broad frequency range. However, they are relatively large, costly, and ineffective at low frequencies.
Mechanical vibration is another related type of noise that commonly creates problems in all areas of
transportation and manufacturing, as well as with many household appliances. S.M. Kuo and D. R. Morgan
2. A Decisive Filtering Selection Approach for Improved Performance Active Noise Cancellation System
www.iosrjournals.org 84 | Page
(1996) in their paper have emphasized the practical aspects of ANC systems in terms of adaptive algorithms and
DSP implementations for real-world applications [4]. Widrow and Hoff (1960) developed the least mean square
algorithm (LMS). This algorithms is a member of stochastic gradient algorithm algorithms, and because of
robustness and low computational complexity, it has been used in wide spectrum of applications [6]. Least
square solution is not very practical in the actual implementation of adaptive filters, this is because we know all
the past samples of the input signal, and as well the desired output signal must be available at every iteration.
Performance with less computational complexity compared to the Second-order VFXLMS algorithm [9]. Debi
Prasad das and G. panda in 2004 has shown a new approach for nonlinear processes using Filtered-s LMS
(FsLMS). They proved that using a nonlinear controller for a linear device can achieve superior performance
when the secondary path is linear non minimum phase and the reference noise is non-gaussian and predictable.
III. PROCEDURE AND APPROACH
The experimental work has been done on MATLAB, audio processor, capable to perform real time
processing on signals. Various program based on individual algorithm were written first e.g. LMS, NLMS,
FSLMS, VSLMS. Each of them is considered as a separate hidden neuron of network. In case two primary noise
signal is chosen to be a logistic chaotic type x(n+1)=λx(n)[1-x(n)] λ=4, x(0)=0,.9[2]
All the neurons were allowed to run for 600, 1200 and 1800 iterations, and respective obtained results were
compared with standard threshold value. LMS algorithm weight update equation is as
w(n+1)=w(n)-µe(n)v(n) (1)
v(n)=x(n)*a(n) where a(n) is approximation of secondary path transfer function. w(n) if filter weight , µ is
step size factor[2]. The calculation of threshold depends upon number of parameter and it may vary according to
surrounding situation and instantaneous noise characteristics. We had adopted threshold parameter δk Which
was based on following equations. For each input Xn
δk(Xn) =exp( -1/σ2( ||XN- µK|||2
)) (2)
Where σ is variance, µ is is weight factor of particular neuron [5]. This approach is based on technique of RBF
neural network. We are using this technique to prune less effective neurons from network and finally running
systems with successful neuron only. In different conditions different neuron may give better results. The
survival chances of neurons depend upon its algorithm efficiency in given circumstances. The step µwas size
varied factor between .05, 0.125 and 0.5 depending upon the optimization required between complexity and
amount of noise cancellation. We have done experimental studies on various inputs for different algorithms e.g.
LMS, NLMS, VSLMS, VSNLMS etc. Finally for simulations trial we opted one particular acoustic signal of
linear nature, which was producing a sound in the frequency range of low decibels.
IV. RESULT AND ANALYSIS
0 100 200 300 400 500 600
0.05
0.055
0.06
0.065
0.07
0.075
0.08
Mean square error convergence for LMS algorithm for step size =0.125
Number of Iteration
MeanSquareError
0 100 200 300 400 500 600
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
MSE for NLMS algorithm for step size 0.125
No. of iterations
MeanSquareError
0 200 400 600 800 1000 1200
-1.5
-1
-0.5
0
0.5
1
1.5
MSE for NLMS algorithm for step size 0.125
No. of iterations
MeanSquareError
0 100 200 300 400 500 600
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
MSE for VSLMS algorithm for the step size 0.125
No. of iterations
MeanSquareError
4. A Decisive Filtering Selection Approach for Improved Performance Active Noise Cancellation System
www.iosrjournals.org 86 | Page
Figure 3. Graphical analysis of variations in mean error for algorithms for number of iterations=600, 1200 and 1800.
V. COMPUTATION AND COMPLEXITY
Application of RBF neural network criteria to eliminate non efficient neuron where neuron is the
different algorithm, for the selection of proficient algorithm can be selected for ANC. The performance of the
algorithm depends upon the type of input. We found VSNLMS is doing better corresponding to the input
„sound‟. Here we used and apply the criteria for deciding threshold value [1]. And the simplified formula is
given as
( 3)
where µk(x) is response of kth
hidden unit. In our case, implementation of the concept of RBF neural network
approach can be used after simplification σ = 1 , and mean square error µ variance is normalized by factor 1/20
Threshold criteria results in selection of the efficiently working neuron as efficient algorithm can be given in
simplified manner, where Th is the threshold value and MSE is Mean square error 1/20 is the normalizing factor
as :
(4)
VI. CONCLUSION AND FUTURE WORK
It was found out those in particular noisy conditions most suitable algorithm can be sort out using
decisive method using Radial basis function in ANC method. This method can also be applied to noise
cancellation, vibration cancellation and signal estimation problems. Average of the MSE obtained 0.3175
deviations from mean when calculated; it is observed that Variable step size Normalized LMS is performing
better than the other utilized application for ANC of “Sound” input for no. of iterations 1800 with step size 0.5
as the threshold criteria results =Mean error-(1/20)Average. 0.0068 is the least value amongst the all applied
algorithm, hence pruning of all other algorithm inculcate the selection of that algorithm as survival neuron.
Next good performing algorithm is NLMS itself for 600 iterations of step size 0.05. The surprisingly good
performance of the LMS is due to linear nature of the „sound‟ input noise. Here in this project have tried to
reduce such weed signals by using basic adaptive approach. But in real time applications which involve
thousands of such weed signals and knowing the fact that noise, at any cost, cannot be cancelled to cent percent,
this basic approach cannot do us a good favour and keeps this research topic active in upcoming years. The
future task is to apply this novel concept for Kalman and Wiener filter‟s family algorithm simultaneously. It is
also being tried to implement it for outer noise removal in automobiles and noise cancellation in head phones.
Noise performance can be upgraded by finding proper step factor and threshold value. The algorithm level up
gradation of threshold and decisive criteria may improve performance
ACKNOWLEDGEMENTS
We pay our special thanks to Mr. Himanshu Purohit, Prof. D. Das, for sharing good research ideas and
suggestions. We wish to put on record the appreciative original work of all the authors of various technical
papers which we have referred in initiation of work without whom it was very difficult to achieve successful
completion.
5. A Decisive Filtering Selection Approach for Improved Performance Active Noise Cancellation System
www.iosrjournals.org 87 | Page
REFERENCES
[1] S.M. Kuo and D.R.Morgan, Active Noise Control –Algorithms and DSP Implementations. New York: Wiley, 1996.
[2] Lu Yingwei, N.Sundararajan, P.Saratchandran “Adaptive Nonlinear Systems Identification Using Minimal Radial Basis Function
Neural Networks “in IEEE 1996.
[3] Shoji Makino, Member, IEEE, Yutaka Kaneda, Member, IEEE and Nobuo Koizumi, “Exponentially weighted step size NLMS
adaptive filter based on the statistics of a room impulse response”, IEEE Trans. on speech and audio Processing, vol. 1, No.1,
pp.101-108, Jan 1993.
[4] S. Qureshi, “Adaptive Equalization,” Proceedings of the IEEE, vol.73, No.9, pp.1349-1387, Sept. 1985.
[5] S. Haykins, “Adaptive Filter Theory”, Prentice Hall, New Jersey, 1996.
[6] Raymond H. Kwong and Edward W. Johnston, “A Variable Step Size LMS Algorithm”, IEEE Transactions on Signal Processing vol.
40, No.7, pp. 1633-1642, July, 1992.
[7] Tyseer Aboulnasr, Member, IEEE, and K.Mayyas, “A robust variable step size LMS type algorithm: Analysis and Simulations”,
IEEE Trans. On Signal Processing vol. 45, No.3, pp. 631-639, March 1997.
[8] Dargar S K, Himanshu Purohit, Mahajan “An Iterative Pruning Approach of Neural Network for Proficient Noise Cancellation” in
International Journal Engg Advanced Technology, ISSN: 2249 – 8958, Volume-2, Issue-4, April 2013.
[9] Debi Prasad Das ,”Filtered –s LMS Algorithm for Multichannel Active control Of Nonlinear Noise Processes” in IEEE Transactions
on Speech And Audio Processing , vol 14 ,no.5 September 2006.
[10] Debi Das and Ganpati Panda, “Active Mitigation of Nonlinear Noise Processes Using A Novel Filtered-s LMS Algorithm” in IEEE
Transactions of Speech and Audio Processing, vol. 12, No.3, May 2004.
Abha Nyati1
, is M. Tech scholar from Pacific University PAHER, Udaipur pursuing specialization in Digital
Communication. She has completed her degree of Bachelor of Engineering in electronics and communication with
honors from Mohanlal Sukhadia University, Rajasthan. She has good academic records and has academic
experience in teaching. Her area of research interest is digital communication, signal processing and wireless
communication.
Shashi Kant Dargar2
, is working as assistant professor in the department of Electronics and Communication
Engineering at Sir Padampat Singhania University, Udaipur. Presently, and pursuing Ph.D. He has completed his
degree of Bachelor of Engineering from University of Rajasthan, Jaipur in specialization with Electronics and
Communication Engineering. He has completed his degree of Masters of Technology in Digital Communication
from Rajasthan Technical University, Kota. He has been contributing in academics from seven years. His Area of
research interest is signal processing, wireless communication, Microwave & Communication Engineering.
**Deepak Vyas, is working as assistant professor in the department of Electronics and Communication Engineering
at Pacific Institute of Technology, Udaipur. Presently, he is pursuing Ph.D. He has completed his degree of Bachelor
of Engineering in specialization with Electronics and Communication Engineering. He has completed his degree of
Masters of Technology in Digital Communication from Rajasthan Technical University, Kota. He has been
contributing in academics form over seven years. His Area of research interest is Communication Engineering. He
has guided many projects for undergraduate and post graduate students in his academic career.