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®.
echo types, how to cancel echo in each type, which is more complex, echo cancellation implementation in matlab
prepared by : OLA MASHAQI ,, SUHAD MALAYSHE
Application of Digital Signal Processing In Echo Cancellation: A SurveyEditor IJCATR
The advanced communications world is worried talking more naturally by using hands free this help the human being to talk
more confidently without holding any of the devices such as microphones or telephones. Acoustic echo cancellation and noise
cancellers are quite interesting nowadays because they are required in many applications such as speakerphones and audio/video
conferencing. This paper describes an alternative method of estimating signals corrupted by additive noise or interference. Acoustic
echo cancellation problem was discussed out of different noise cancellation techniques by concerning different parameters with their
comparative results .The results shown are using some specific 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 types, how to cancel echo in each type, which is more complex, echo cancellation implementation in matlab
prepared by : OLA MASHAQI ,, SUHAD MALAYSHE
Application of Digital Signal Processing In Echo Cancellation: A SurveyEditor IJCATR
The advanced communications world is worried talking more naturally by using hands free this help the human being to talk
more confidently without holding any of the devices such as microphones or telephones. Acoustic echo cancellation and noise
cancellers are quite interesting nowadays because they are required in many applications such as speakerphones and audio/video
conferencing. This paper describes an alternative method of estimating signals corrupted by additive noise or interference. Acoustic
echo cancellation problem was discussed out of different noise cancellation techniques by concerning different parameters with their
comparative results .The results shown are using some specific 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.
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.
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
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.
Real-Time Active Noise Cancellation with Simulink and Data Acquisition ToolboxIDES Editor
This paper presents the feasibility of implementing
single channel negative feedback Active Noise Cancellation
technique using adaptive filters in Real-time environment[1].
In order to establish the suitability and credibility of LMS
Algorithm for adaptive filtering in real world scenario, its
efficiency was tested beyond system based ideal simulations.
Within the MATLAB® software environment two different
methods were used to perform Real-time ANC namely
Simulink® and Data Acquisition ToolboxTM. Human voice is
used as test signal. For processing and performing adaptive
filtering, Block LMS Filter was utilised in Simulink and Error
Normalised Step Size algorithm was used in between input
and output of Signals by DAQ (Data Acquisition) toolbox
interface. A general method of using DAQ commands has been
employed which also allows for almost any kind of complex
real-time audio processing and is quite easy to follow.
the presentation consists of a brief description about ADAPTIVE LINEAR EQUALIZER , its classification and the associated attributes of ZERO FORCING EQUALIZER and MMSE EQUALIZER
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.
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.
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.
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
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.
Real-Time Active Noise Cancellation with Simulink and Data Acquisition ToolboxIDES Editor
This paper presents the feasibility of implementing
single channel negative feedback Active Noise Cancellation
technique using adaptive filters in Real-time environment[1].
In order to establish the suitability and credibility of LMS
Algorithm for adaptive filtering in real world scenario, its
efficiency was tested beyond system based ideal simulations.
Within the MATLAB® software environment two different
methods were used to perform Real-time ANC namely
Simulink® and Data Acquisition ToolboxTM. Human voice is
used as test signal. For processing and performing adaptive
filtering, Block LMS Filter was utilised in Simulink and Error
Normalised Step Size algorithm was used in between input
and output of Signals by DAQ (Data Acquisition) toolbox
interface. A general method of using DAQ commands has been
employed which also allows for almost any kind of complex
real-time audio processing and is quite easy to follow.
the presentation consists of a brief description about ADAPTIVE LINEAR EQUALIZER , its classification and the associated attributes of ZERO FORCING EQUALIZER and MMSE EQUALIZER
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.
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.
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.
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.
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.
DESIGN REALIZATION AND PERFORMANCE EVALUATION OF AN ACOUSTIC ECHO CANCELLATIO...sipij
Nowadays, in the field of communications, AEC (acoustic echo cancellation) is truly essential with respect
to the quality of multimedia transmission. In this paper, we designed and developed an efficient AEC based
on adaptive filters to improve quality of service in telecommunications against the phenomena of acoustic
echo, which is indeed a problem in hands-free communications.The main advantage of the proposed algorithm is its capacity of tracking non-stationary signals such as acoustic echo. In this work the acoustic echo cancellation (AEC) is modeled using a digital signal
processing technique especially Simulink Blocksets. The algorithm’s code is generated in Matlab Simulink
programming environment. At simulation level, results of simulink implementation prove that module
behavior is realistic when it comes to cancellation of echo in hands free communication using adaptive algorithm.Results obtained with our algorithm in terms of ERLE criteria are confronted to IUT-T recommendation
G.168.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Decisive Filtering Selection Approach For Improved Performance Active Noise...IOSR Journals
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.
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.
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.
LMS Adaptive Filters for Noise Cancellation: A Review IJECEIAES
This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.
Noise Cancellation in ECG Signals using ComputationallyCSCJournals
Several signed LMS based adaptive filters, which are computationally superior having multiplier free weight update loops are proposed for noise cancellation in the ECG signal. The adaptive filters 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: 60Hz power line interference, baseline wander, muscle noise and the motion artifact. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the signed regressor LMS algorithm is superior than conventional LMS algorithm, the performance of signed LMS and sign-sign LMS based realizations are comparable to that of the LMS based filtering techniques in terms of signal to noise ratio and computational complexity.
METHOD FOR REDUCING OF NOISE BY IMPROVING SIGNAL-TO-NOISE-RATIO IN WIRELESS LANIJNSA Journal
The signal to noise ratio (SNR) is one of the important measures for reducing the noise.A technique that uses a linear prediction error filter (LPEF) and an adaptive digital filter (ADF) to achieve noise reduction in a speech and image degraded by additive background noise is proposed. Since a speech signal can be represented as the stationary signal over a short interval of time, most of speech signal can be predicted by the LPEF. This estimation is performed by the ADF which is used as system identification. Noise reduction is achieved by subtracting the reconstructed noise from the speech degraded by additive background noise. Most of the MR image accelerating methods suffers from degradation of acquired images, which is often correlated with the degree of acceleration. However, Wideband MRI is a novel technique that transcends such flaws.In this paper we proposed LPEF and ADF for reducing the noise in speech and also we demonstrate that Wideband MRI is capable of obtaining images with identical quality as conventional MR images in terms of SNR in wireless LAN.
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.
Similar to Echo Cancellation Algorithms using Adaptive Filters: A Comparative Study (20)
Now-a-days, Internet has become an important part of human’s life, a person
can shop, invest, and perform all the banking task online. Almost, all the organizations have
their own website, where customer can perform all the task like shopping, they only have to
provide their credit card details. Online banking and e-commerce organizations have been
experiencing the increase in credit card transaction and other modes of on-line transaction.
Due to this credit card fraud becomes a very popular issue for credit card industry, it causes
many financial losses for customer and also for the organization. Many techniques like
Decision Tree, Neural Networks, Genetic Algorithm based on modern techniques like
Artificial Intelligence, Machine Learning, and Fuzzy Logic have been already developed for
credit card fraud detection. In this paper, an evolutionary Simulated Annealing algorithm is
used to train the Neural Networks for Credit Card fraud detection in real-time scenario.
This paper shows how this technique can be used for credit card fraud detection and
present all the detailed experimental results found when using this technique on real world
financial data (data are taken from UCI repository) to show the effectiveness of this
technique. The algorithm used in this paper are likely beneficial for the organizations and
for individual users in terms of cost and time efficiency. Still there are many cases which are
misclassified i.e. A genuine customer is classified as fraud customer or vise-versa.
Wireless sensor networks (WSN) have been widely used in various applications.
In these networks nodes collect data from the attached sensors and send their data to a base
station. However, nodes in WSN have limited power supply in form of battery so the nodes
are expected to minimize energy consumption in order to maximize the lifetime of WSN. A
number of techniques have been proposed in the literature to reduce the energy
consumption significantly. In this paper, we propose a new clustering based technique
which is a modification of the popular LEACH algorithm. In this technique, first cluster
heads are elected using the improved LEACH algorithm as usual, and then a cluster of
nodes is formed based on the distance between node and cluster head. Finally, data from
node is transferred to cluster head. Cluster heads forward data, after applying aggregation,
to the cluster head that is closer to it than sink in forward direction or directly to the sink.
This reduction in distance travelled improves the performance over LEACH algorithm
significantly.
The next generation wireless networks comprises of mobile users moving
between heterogeneous networks, using terminals with multiple access interfaces and
services. The most important issue in such environment is ABC (Always Best Connected) i.e.
allowing the best connectivity to applications anywhere at any time. For always best
connectivity requirement various vertical handover strategies for decision making have
been proposed. This paper provides an overview of the most interesting and recent
strategies.
This paper presents the design and performance comparison of a two stage
operational amplifier topology using CMOS and BiCMOS technology. This conventional op
amp circuit was designed by using RF model of BSIM3V3 in 0.6 μm CMOS technology and
0.35 μm BiCMOS technology. Both the op amp circuits were designed and simulated,
analyzed and performance parameters are compared. The performance parameters such as
gain, phase margin, CMRR, PSRR, power consumption etc achieved are compared. Finally,
we conclude the suitability of CMOS technology over BiCMOS technology for low power
RF design.
In Cognitive Radio Networks (CRN), Cooperative Spectrum Sensing (CSS) is
used to improve performance of spectrum sensing techniques used for detection of licensed
(Primary) user’s signal. In CSS, the spectrum sensing information from multiple unlicensed
(Secondary) users are combined to take final decision about presence of primary signal. The
mixing techniques used to generate final decision about presence of PU’s signal are also
called as Fusion techniques / rules. The fusion techniques are further classified as data
fusion and decision fusion techniques. In data fusion technique all the secondary users
(SUs) share their raw information of spectrum detection like detected energy or other
statistical information, while in decision fusion technique all the SUs take their local
decisions and share the decision by sending ‘0’ or ‘1’ corresponding to absence and presence
of PU’s signal respectively. The rules used in decision fusion techniques are OR rule, AND
rule and K-out-of-N rule. The CSS is further classified as distributed CSS and centralized
CSS. In distributed CSS all the SUs share the spectrum detection information with each
other and by mixing the shared information; all the SUs take final decision individually. In
centralized CSS all the SUs send their detected information to a secondary base station /
central unit which combines the shared information and takes final decision. The secondary
base station shares the final decision with all the SUs in the CRN. This paper covers
overview of information fusion methods used for CSS and analysis of decision fusion rules
with simulation results.
ZigBee has been developed to support lower data rates and low power consuming
applications. This paper targets to analyze various parameters of ZigBee physical (PHY).
Performance of ZigBee PHY is evaluated on the basis of energy consumption in
transmitting and receiving mode and throughput. Effect of variation in network size is
studied on these performance attributes. Some modulation schemes are also compared and
the best modulation scheme is suggested with tradeoffs between different performance
metrics.
This paper gives a brief idea of the moving objects tracking and its application.
In sport it is challenging to track and detect motion of players in video frames. Task
represents optical flow analysis to do motion detection and particle filter to track players
and taking consideration of regions with movement of players in sports video. Optical flow
vector calculation gives motion of players in video frame. This paper presents improved
Luacs Kanade algorithm explained for optical flow computation for large displacement and
more accuracy in motion estimation.
A rapid progress is seen in the field of robotics both in educational and industrial
automation sectors. The Robotics education in particular is gaining technological advances
and providing more learning opportunities. In automotive sector, there is a necessity and
demand to automate daily human activities by robot. With such an advancement and
demand for robotics, the realization of a popular computer game will help students to learn
and acquire skills in the field of robotics. The computer game such as Pacman offers
challenges on both software and hardware fronts. In software, it provides challenges in
developing algorithms for a robot to escape from the pool of attacking robots and to develop
algorithms for multiple ghost robots to attack the Pacman. On the hardware front, it
provides a challenge to integrate various systems to realize the game. This project aims to
demonstrate the pacman game in real world as well as in simulation. For simulation
purpose Player/Stage is used to develop single-client and multi-client architectures. The
multi- client architecture in player/stage uses one global simulation proxy to which all the
robot models are connected. This reduces the overhead to manage multiple robots proxy.
The single-client architecture enables only two robot models to connect to the simulation
proxy. Multi-client approach offers flexibility to add sensors to each port which will be used
distinctly by the client attached to the respective robot. The robots are named as Pacman
and Ghosts, which try to escape and attack respectively. Use of Network Camera has been
done to detect the global positions of the robots and data is shared through inter-process
communication.
In Content-Based Image Retrieval (CBIR) systems, the visual contents of the
images in the database are took out and represented by multi-dimensional characteristic
vectors. A well known CBIR system that retrieves images by unsupervised method known
as cluster based image retrieval system. For enhancing the performance and retrieval rate
of CBIR system, we fuse the visual contents of an image. Recently, we developed two
cluster-based CBIR systems by fusing the scores of two visual contents of an image. In this
paper, we analyzed the performance of the two recommended CBIR systems at different
levels of precision using images of varying sizes and resolutions. We also compared the
performance of the recommended systems with that of the other two existing CBIR systems
namely UFM and CLUE. Experimentally, we find that the recommended systems
outperform the other two existing systems and one recommended system also comparatively
performed better in every resolution of image.
Information Systems and Networks are subjected to electronic attacks. When
network attacks hit, organizations are thrown into crisis mode. From the IT department to
call centers, to the board room and beyond, all are fraught with danger until the situation is
under control. Traditional methods which are used to overcome these threats (e.g. firewall,
antivirus software, password protection etc.) do not provide complete security to the system.
This encourages the researchers to develop an Intrusion Detection System which is capable
of detecting and responding to such events. This review paper presents a comprehensive
study of Genetic Algorithm (GA) based Intrusion Detection System (IDS). It provides a
brief overview of rule-based IDS, elaborates the implementation issues of Genetic Algorithm
and also presents a comparative analysis of existing studies.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Brain tumor is a malformed growth of cells within brain which may be
cancerous or non-cancerous. The term ‘malformed’ indicates the existence of tumor. The
tumor may be benign or malignant and it needs medical support for further classification.
Brain tumor must be detected, diagnosed and evaluated in earliest stage. The medical
problems become grave if tumor is detected at the later stage. Out of various technologies
available for diagnosis of brain tumor, MRI is the preferred technology which enables the
diagnosis and evaluation of brain tumor. The current work presents various clustering
techniques that are employed to detect brain tumor. The classification involves classification
of images into normal and malformed (if detected the tumor). The algorithm deals with
steps such as preprocessing, segmentation, feature extraction and classification of MR brain
images. Finally, the confirmatory step is specifying the tumor area by technique called
region of interest.
A Proxy signature scheme enables a proxy signer to sign a message on behalf of
the original signer. In this paper, we propose ECDLP based solution for chen et. al [1]
scheme. We describe efficient and secure Proxy multi signature scheme that satisfy all the
proxy requirements and require only elliptic curve multiplication and elliptic curve addition
which needs less computation overhead compared to modular exponentiations also our
scheme is withstand against original signer forgery and public key substitution attack.
Water marking has been proposed as a method to enhance data security. Text
water marking requires extreme care when embedding additional data within the images
because the additional information must not affect the image quality. Digital water marking
is a method through which we can authenticate images, videos and even texts. Add text
water mark and image water mark to your photos or animated image, protect your
copyright avoid unauthorized use. Water marking functions are not only authentication, but
also protection for such documents against malicious intentions to change such documents
or even claim the rights of such documents. Water marking scheme that hides water
marking in method, not affect the image quality. In this paper method of hiding a data using
LSB replacement technique is proposed.
Today among various medium of data transmission or storage our sensitive data
are not secured with a third-party, that we used to take help of. Cryptography plays an
important role in securing our data from malicious attack. This paper present a partial
image encryption based on bit-planes permutation using Peter De Jong chaotic map for
secure image transmission and storage. The proposed partial image encryption is a raw data
encryption method where bits of some bit-planes are shuffled among other bit-planes based
on chaotic maps proposed by Peter De Jong. By using the chaotic behavior of the Peter De
Jong map the position of all the bit-planes are permuted. The result of the several
experimental, correlation analysis and sensitivity test shows that the proposed image
encryption scheme provides an efficient and secure way for real-time image encryption and
decryption.
This paper presents a survey of Dependency Analysis of Service Oriented
Architecture (SOA) based systems. SOA presents newer aspects of dependency analysis due
to its different architectural style and programming paradigm. This paper surveys the
previous work taken on dependency analysis of service oriented systems. This study shows
the strengths and weaknesses of current approaches and tools available for dependency
analysis task in context of SOA. The main motivation of this work is to summarize the
recent approaches in this field of research, identify major issue and challenges in
dependency analysis of SOA based systems and motivate further research on this topic.
In this paper, proposed a novel implementation of a Soft-Core system using
micro-blaze processor with virtex-5 FPGA. Till now Hard-Core processors are used in
FPGA processor cores. Hard cores are a fixed gate-level IP functions within the FPGA
fabrics. Now the proposed processor is Soft-Core Processor, this is a microprocessor fully
described in software, usually in an HDL. This can be implemented by using EDK tool. In
this paper, developed a system which is having a micro-blaze processor is the combination
of both hardware & Software. By using this system, user can control and communicate all
the peripherals which are in the supported board by using Xilinx platform to develop an
embedded system. Implementing of Soft-Core process system with different peripherals like
UART interface, SPA flash interface, SRAM interface has to be designed using Xilinx
Embedded Development Kit (EDK) tools.
The article presents a simple algorithm to construct minimum spanning tree and
to find shortest path between pair of vertices in a graph. Our illustration includes the proof
of termination. The complexity analysis and simulation results have also been included.
Wimax technology has reshaped the framework of broadband wireless internet
service. It provides the internet service to unconnected or detached areas such as east South
Africa, rural areas of America and Asia region. Full duplex helpers employed with one of
the relay stations selection and indexing method that is Randomized Distributed Space Time
are used to expand the coverage area of primary Wimax station. The basic problem was
identified at cell edge due to weather conditions (rain, fog), insertion of destruction because
of multiple paths in the same communication channel and due to interference created by
other users in that communication. It is impractical task for the receiver station to decode
the transmitted signal successfully at the cell edges, which increases the high packet loss and
retransmissions. But Wimax is a outstanding technology which is used for improving the
quality of internet service and also it offers various services like Voice over Internet
Protocol, Video conferencing and Multimedia broadcast etc where a little delay in packet
transmission can cause a big loss in the communication. Even setup and initialization of
another Wimax station nearer to each other is not a good alternate, where any mobile
station can easily handover to another base station if it gets a strong signal from other one.
But in rural areas, for few numbers of customers, installation of base station nearer to each
other is costlier task. In this review article, we present a scheme using R-DSTC technique to
choose and select helpers (relay nodes) randomly to expand the coverage area and help to
mobile station as a helper to provide secure communication with base station. In this work,
we use full duplex helpers for better utilization of bandwidth.
Radio Frequency identification (RFID) technology has become emerging
technique for tracking and items identification. Depend upon the function; various RFID
technologies could be used. Drawback of passive RFID technology, associated to the range
of reading tags and assurance in difficult environmental condition, puts boundaries on
performance in the real life situation [1]. To improve the range of reading tags and
assurance, we consider implementing active backscattering tag technology. For making
mobiles of multiple radio standards in 4G network; the Software Defined Radio (SDR)
technology is used. Restrictions in Existing RFID technologies and SDR technology, can be
eliminated by the development and implementation of the Software Defined Radio (SDR)
active backscattering tag compatible with the EPC global UHF Class 1 Generation 2 (Gen2)
RFID standard. Such technology can be used for many of applications and services.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
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The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
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Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Create Map Views in the Odoo 17 ERPCeline George
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Model Attribute Check Company Auto PropertyCeline George
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
2. As depicted in Fig. 2, direct signal d from the source S at height h reaches the listener L which is
followed by the reflected signals r having the magnitude almost same as direct signal. It is referred to as
Echo signal. It is formed when the direct signal hits the obstacles in the room and gets reflected. Such an
echo signal needs to be eliminated or suppressed for better signal perception [2].
For echo cancellation, adaptive filters driven by an error signal are used. Adaptive filters have adjustable
filter parameters to minimize the undesired signal by using an adaptive algorithm. There are numerous
adaptive algorithms used in an adaptive filter, out of which LMS (Least Mean Square) Algorithm, NLMS
(Normalized Least Mean Square) Algorithm, RLS (Recursive Least Square) Algorithm are prominent and
widely used.
Spectrogram [3] is the display of the magnitude of the Short-Time Fourier Transform. In the spectrogram
display, the x-axis represents the time-index and y-axis represents the frequency, whereas the magnitude is
represented by the darkness of the plot.
ERLE [4] (Echo Return Loss Enhancement) is defined as the ratio of the power of the desired signal over the
power of the residual signal. It is a smoothed measure of the amount (in dB) that the echo has been
attenuated. ERLE should stabilize in the interval [-40dB, 30dB] for a good performance. ERLE is used to
measure the potential of echo cancellation.
Cross-correlation estimates the similarity between desired signal and echo cancelled signal.
This paper performs acoustical echo cancellation using an adaptive filter driven by LMS, NLMS and RLS
algorithm and analyses the echo cancelled/suppressed signals obtained from adaptive filter output using
spectrogram, ERLE, cross-correlation and come out with a better algorithm among LMS, NLMS and RLS
algorithm for acoustical echo cancellation.
II. LITERATURE REVIEW
This section discusses the literature review of adaptive filters, echo and the process of echo cancellation.
A. Adaptive Digital Filters
Adaptive filters are self-learning filters, whereby an FIR or IIR filter is designed based on the characteristics
of input signals to adapt its environment. The environment will be defined by the input signal x n and
desired signal d n . Adaptive filters have self-regulation and tracking capabilities. An adaptive filter finds its
essence in applications such as Echo Cancellation, Noise Cancellation, System Identification and many
others. A basic adaptive filter was first invented at AT&T Bell Labs. Since its inception, several adaptive
filter algorithms were designed and honed. A few algorithms include LMS, NLMS and RLS algorithms.
These algorithms were designed to anticipate the signal which would inevitably re-enter the transmission
path and cancel it out.
A few adaptive filter algorithms are discussed below:
1. LMS (Least Mean Square) Algorithm [5] - It is a stochastic gradient-based algorithm introduced by
Bernard Widrow and Ted Hoff which uses gradient vector of the filter tap weights in order to converge on
the optimal Wiener solution. In each iteration of the algorithm, the filter taps weights are updated as per
w n represents the adaptive filter weight vector at time n , x n represents timedelayed input signal samples, e n represents error signal to be minimized and
represents step size or
Equation (3) where
convergence factor.
y ( n ) w h x ( n)
Error, e (n ) d ( n) y ( n)
Weight, w(n 1) w( n)
Output,
(1)
(2)
x ( n)e( n) (3)
If
is chosen to be very small then the algorithm converges very slowly. A large value of may lead to a
faster convergence but the adaptive filter becomes less stable around the minimum value and its output
diverges.
2. NLMS (Normalized LMS) Algorithm [5] – In LMS algorithm, when the values of
is large, the algorithm
experiences a gradient noise amplification problem. This problem is solved by NLMS algorithm. The
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3. w n at iteration n 1 is normalized with respect to the squared
Euclidian norm of the input vector x n at iteration n . The NLMS algorithm can be viewed as a timecorrection applied to weight vector
varying step-size algorithm, calculating the convergence factor
( n)
c
x ( n)
as in Equation (4).
(4)
2
In Equation (4), is the NLMS adaption constant, which optimize the convergence rate of the algorithm and
should satisfy the condition 0
2 , and c is the constant term for normalization, which is always less
than 1.
The filter weights using NLMS algorithm are updated as given in Equation (5).
w(n 1)
w(n)
c
x ( n)
2
e( n ) x ( n )
(5)
3. RLS (Recursive Least Square) Algorithm [6] – This algorithm attempts to minimize the cost function in
Equation (6). In Equation (6), k 1 is the time at which the RLS algorithm commences and
is a small
positive constant very close to, but smaller than 1. With values of
1 more recent input samples, this
results in a scheme that places more emphasis on recent samples of observed data and tends to forget the past.
n
n k
n
k
e
n
k 1
k
(6)
When compared to LMS algorithm, RLS algorithm offers a faster convergence and lower error at steady
state. But, this RLS algorithm is much more computationally complex and if proper design procedures are
not followed, RLS algorithm may diverge away resulting in instability.
B. Echo [7]
Echoes are simply generated by delay units. The direct sound and a single echo appearing after R sampling
periods later can be generated by the FIR filter as shown in Fig. 3.
Figure 3. Echo filter
The transfer function of the echo filter is given by Equation (7).
Z R,
1
(7)
In the above transfer function, the delay parameter R denotes the time the sound wave takes to travel from
H (Z ) 1
the sound source to the listener after bouncing back from the reflecting wall, whereas the parameter ,
with
1 , represents the signal loss caused by propagation and reflection.
There are two types of Echo – Acoustic Echo and Hybrid Echo. Hybrid Echo is generated in PSTN Network.
C. Echo Cancellation
Echo cancellation is the process of removing echo signals from a voice communication system in order to
achieve quality audio perception. The development of echo reduction began in the late 1950s, and continues
today as new integrated landline and wireless cellular networks put additional requirement on the
38
4. performance of echo cancellers. Echo cancellation involves in first recognizing the originally transmitted
signal that re-appears, with some delay, in the transmitted or received signal. Once the echo is recognized, it
is removed by 'subtracting' it from the transmitted or received signal. This technique is usually implemented
on DSP’s using adaptive filters.
III. DIGITAL IMPLEMENTATION USING MATLAB -S IMULINK®
This section describes the digital implementation of various echo cancellation algorithms using MATLABSIMULINK® V7.5. We employ SIMULINK® Signal Processing Toolbox and the common blocks used were
From Multimedia File, Delay, Signal to Workspace, Gain, Sum, To Audio Device, LMS Filter, NLMS Filter
and RLS Filter.
A. Echo Model [7]
This model generates Echo signal for as given input signal. Here we use PCM 16 bit signed, 352 kbps,
22050Hz speech signal as input. The Echo – Simulink model is represented in Fig. 4.
Figure 4. Echo – Simulink model
B. Echo Cancellation Model
We implement Echo Cancellation model using LMS, NLMS and RLS algorithms which are shown in Fig. 6,
Fig. 7, and Fig. 8 respectively. Fig. 5 represents LMS filter Simulink block.
Figure 5. LMS Filter – Simulink Block model
The above block has the below input and output ports.
Input Port
:
Signal + its Echo
Desired Port
:
Desired Audio signal
Output Port
:
Echo cancelled/suppressed signal
Error Port
:
Difference between desired signal and adaptive filter output
Figure 6. LMS Echo Cancellation – Simulink model
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5. Figure 7. NLMS Echo Cancellation – Simulink model
Figure 8. RLS Echo Cancellation – Simulink model
C. Experimental/Implementation Procedure:
STAGE 1:
1) Initially, a speech signal without any echo components is used as desired/ideal signal. It is a PCM
(Pulse Code Modulation) signed 16-bit, 352kbps, 22050Hz, 1 channel speech signal.
2) Echo Model is implemented using Simulink and the value of is kept constant at 0.5 and the value
of R (delay) is kept constant at 8000 samples throughout the experiment. Generated signal is an echo
signal.
3) The desired signal and signal+Echo will be used as inputs to LMS Echo Canceller.
4) Simulation is run for 8 seconds and the Output port of LMS block gives the echo cancelled signal.
Error Port of LMS block gives the difference between desired and LMS output.
5) Desired signal, Signal+Echo, Output signal and Error signal are saved in the workspace for further
analysis.
6) Spectrogram Plots are obtained for desired and output signals using SPECTROGRAM V.5.0 Tool [8]
7) The same procedure is repeated for NLMS and RLS Echo Canceller Algorithms
STAGE 2:
1) We use the concept of ERLE [4] (Echo Return Loss Estimation) to measure the potential of Echo
cancellation. It is defined as the ratio of the power of the desired signal over the power of the residual
signal. The expression to determine ERLE is given in Equation (8).
ERLE
10 log 10
E (d 2 (n))
dB
E (e 2 (n))
(8)
2) It is a smoothed measure of the amount (in dB) that the echo has been attenuated. ERLE should
stabilize in the interval [-40dB, 30dB] for a good performance
STAGE 3:
1) To determine the amount of time shift between desired and echo cancelled signal, we use the concept of
cross-correlation
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6. 2) Cross correlation is calculated between desired signal and echo cancelled output obtained from LMS,
NLMS and RLS adaptive filter algorithms.
3) Ideally, the time shift between desired and echo cancelled signal should be minimal; and the amplitude
of time shifted signal should be very less for better audio perception
TABLE I SPECTROGRAM PLOT
a. Desired Spectrogram
b. LMS Output
c. NLMS Output
d. RLS Output
IV. IMPLEMENTATION ANALYSIS
This section discusses the analysis of Echo cancellation algorithm implemented using Spectrogram, ERLE
and Cross-Correlation.
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7. TABLE II
ERLE PLOT
TABLE III
CROSS- CORRELATION PLOT
a. LMS Output
a. LMS Output and Desired Output
b. NLMS Output
b. NLMS Output and Desired Output
c. RLS Output
c. RLS Output and Desired Output
Spectrogram Analysis – For LMS, NLMS and RLS echo canceller output samples, spectrograms were
determined and the plots are represented in Table I. From the spectrogram plots we observe that the NLMS
cancels the echo signals to a maximum extent and RLS cancels the echo signals to a minimum extent
whereas in LMS algorithm, echo signals are cancelled out moderately.
ERLE Analysis – As discussed earlier, ERLE measures the potential of echo cancellation. It is calculated as
per the Equation (8). The ERLE plots for LMS, NLMS, and RLS algorithms are represented in Table II. For
LMS algorithm, ERLE value lies in the range [-65dB, 50dB]. For NLMS algorithm, ERLE value lies in the
range [-60dB, 40dB] and for RLS algorithm, ERLE value lies in the range [-80dB, 60dB]. But, ERLE value
has to stabilize in the range [- 40dB, 30dB] for better performance. Hence, NLMS algorithm offers better
performance when compared to LMS and RLS.
Cross-Correlation Analysis – It is used to determine the time-shift between two signals. The cross-correlation
plots of LMS, NLMS and RLS algorithms are represented in Table III.
From Table 3 we observe that the amplitude of time shifted signal (Echo signal) is minimum for NLMS and
maximum for RLS, whereas the amplitude of LMS between the two. Hence, NLMS algorithm offers better
echo cancellation.
V. CONCLUSION
Considering the Spectrogram analysis, cross correlation and ERLE results of three adaptive filter algorithms,
this paper concludes that the NLMS algorithm is best suited for echo cancellation. NLMS algorithm provides
42
8. better ERLE stability in the range [-40dB, 30dB]. Also the amplitude of time shifted in Cross Correlation
plots is minimum for NLMS algorithm.
Listening tests indicate that the perceived temporal quality or texture is better for NLMS, followed by LMS
and RLS. This is also evident from Spectrogram, ERLE and Cross-Correlation plots.
This paper also discusses the implementation of LMS, NLMS and RLS adaptive filter algorithms for echo
cancellation in a concert hall and it brings out the difference between LMS, NLMS and RLS algorithms,
Finally, it performs a better analysis of echo cancellation algorithms considering Spectrogram, ERLE and
cross-correlation.
This paper concludes with the NLMS adaptive filter algorithm to be a better algorithm amongst LMS, NLMS
and RLS for echo cancellation.
REFERENCES
[1] Vinay K. Ingle, John G. Proakis, Digital Signal Processing Using Matlab, Third Edition, Cengage Learning, 2012
[2] https://ccrma.stanford.edu/~jos/pasp/Acoustic_Echo_Simulator. html
[3] Sanjit K Mitra, Digital Signal Processing, Third Edition, Tata McGraw-Hill Companies, 2006
[4] Irina Dornean, Marina Topa, Botond Sandor Kirei, Marius Neag, “Sub-Band Adaptive Filtering for Acoustic Echo
Cancellation”, IEEE European Conference on Circuit Theory and Design, pp 810 - 813, 2009
[5] Raj Kumar Thenua, S. K. Agrawal, "Hardware Implementation of Adaptive Algorithms for Noise Cancellation",
International Journal of Information and Electronics Engineering, Vol. 2, No. 2, March 2012
[6] Amit Munjal, Vibha Aggarwal, Gurpal Singh, "RLS Algorithm for Acoustic Echo Cancellation", Proceedings of 2nd
National Conference on Challenges & Opportunities in Information Technology (COIT-2008), RIMT-IET, Mandi
Gobindgarh, pp 299-303, March 29, 2008
[7] Pushpalatha G.S, Shivaputra, Mohan Kumar N, "Spectrogram Study of Echo and Reverberation", Signal Processing
and Information Technology, LNICST, Vol. 62, 2012, pp 210-213
[8] Spectrogram version 5.0, Available: http://smedor.com/gram50.zip
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