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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Simulink based design simulations of band pass fir filtereSAT Journals
Abstract In this paper, window function method is used to design digital filters. The Band Pass filter has been design with help of Simulink in MATLAB, which have better characteristics of devising filter in fast and effective way. The band pass filter has been design and simulated using Kaiser window technique. This model is established by using Simulink in MATLAB and the filtered waveforms are observed by spectrum scope to analyze the performance of the filter. Keywords: FIR, window function method, Kaiser, Simulink, MATLAB.
Analysis the results_of_acoustic_echo_cancellation_for_speech_processing_usin...Venkata Sudhir Vedurla
This document presents an analysis of acoustic echo cancellation for speech processing using the LMS adaptive filtering algorithm. It begins with an abstract that outlines the challenges of conventional echo cancellation techniques and the need for a computationally efficient, rapidly converging algorithm. It then provides background on acoustic echo, the principles of echo cancellation, discrete time signals, speech signals, and an overview of the LMS adaptive filtering algorithm and its application to echo cancellation. The document analyzes the performance of the LMS algorithm for echo cancellation by examining how the step size parameter affects convergence and steady state error. It concludes that the LMS algorithm is well-suited for echo cancellation due to its computational simplicity, though the step size must be carefully selected for optimal performance
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper on echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). The paper proposes using adaptive combination of NSAFs to achieve both fast convergence and low steady-state mean squared error. The input signal is divided into subbands, and NSAFs are adapted independently in each subband. Adaptive combination is then performed by adapting a mixing parameter that controls the combination of subband outputs. Experimental results show the proposed method achieves improved performance over conventional NSAF methods using fewer adaptive filters.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). It presents the following:
1. Fullband adaptive filters can have slow convergence due to correlated speech input and long echo path impulse responses. Subband adaptive filters (SAFs) address this by using individual adaptive filters in spectral subbands.
2. Adaptive combination of SAFs provides a way to achieve both fast convergence and small steady-state error. It independently adapts filters with different step sizes, then combines them using a mixing parameter adapted by stochastic gradient descent.
3. The proposed method adaptively combines NSAFs in subbands. It uses a large step size filter for fast convergence and a
This document discusses the use of an adaptive decision feedback equalizer (ADFE) to mitigate pulse dispersion in optical communication channels. It begins by describing different sources of dispersion in optical fibers. Then it proposes using a fractional spaced decision feedback equalizer (FSDFE) integrated with activity detection guidance (ADG) and tap decoupling (TD) to improve performance. Simulation results show the FSDFE can estimate the channel impulse response and minimize differences between the input and output. Adding ADG and TD further improves convergence rate, detection of inactive taps, and asymptotic performance. The ADFE is an effective technique for equalization and mitigating dispersion in optical links.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Simulink based design simulations of band pass fir filtereSAT Journals
Abstract In this paper, window function method is used to design digital filters. The Band Pass filter has been design with help of Simulink in MATLAB, which have better characteristics of devising filter in fast and effective way. The band pass filter has been design and simulated using Kaiser window technique. This model is established by using Simulink in MATLAB and the filtered waveforms are observed by spectrum scope to analyze the performance of the filter. Keywords: FIR, window function method, Kaiser, Simulink, MATLAB.
Analysis the results_of_acoustic_echo_cancellation_for_speech_processing_usin...Venkata Sudhir Vedurla
This document presents an analysis of acoustic echo cancellation for speech processing using the LMS adaptive filtering algorithm. It begins with an abstract that outlines the challenges of conventional echo cancellation techniques and the need for a computationally efficient, rapidly converging algorithm. It then provides background on acoustic echo, the principles of echo cancellation, discrete time signals, speech signals, and an overview of the LMS adaptive filtering algorithm and its application to echo cancellation. The document analyzes the performance of the LMS algorithm for echo cancellation by examining how the step size parameter affects convergence and steady state error. It concludes that the LMS algorithm is well-suited for echo cancellation due to its computational simplicity, though the step size must be carefully selected for optimal performance
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a research paper on echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). The paper proposes using adaptive combination of NSAFs to achieve both fast convergence and low steady-state mean squared error. The input signal is divided into subbands, and NSAFs are adapted independently in each subband. Adaptive combination is then performed by adapting a mixing parameter that controls the combination of subband outputs. Experimental results show the proposed method achieves improved performance over conventional NSAF methods using fewer adaptive filters.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document discusses echo cancellation using adaptive combination of normalized subband adaptive filters (NSAFs). It presents the following:
1. Fullband adaptive filters can have slow convergence due to correlated speech input and long echo path impulse responses. Subband adaptive filters (SAFs) address this by using individual adaptive filters in spectral subbands.
2. Adaptive combination of SAFs provides a way to achieve both fast convergence and small steady-state error. It independently adapts filters with different step sizes, then combines them using a mixing parameter adapted by stochastic gradient descent.
3. The proposed method adaptively combines NSAFs in subbands. It uses a large step size filter for fast convergence and a
This document discusses the use of an adaptive decision feedback equalizer (ADFE) to mitigate pulse dispersion in optical communication channels. It begins by describing different sources of dispersion in optical fibers. Then it proposes using a fractional spaced decision feedback equalizer (FSDFE) integrated with activity detection guidance (ADG) and tap decoupling (TD) to improve performance. Simulation results show the FSDFE can estimate the channel impulse response and minimize differences between the input and output. Adding ADG and TD further improves convergence rate, detection of inactive taps, and asymptotic performance. The ADFE is an effective technique for equalization and mitigating dispersion in optical links.
This document describes a study that introduces a Modified Error Data Normalized Step Size (MEDNSS) algorithm for an adaptive noise canceller. The MEDNSS algorithm uses a time-varying step size that depends on normalization of both the error and data vectors. The performance of the MEDNSS algorithm is analyzed through computer simulation and compared to the Error Data Normalized Step Size algorithm in stationary and non-stationary environments with different noise power levels. Simulation results show the MEDNSS algorithm significantly improves minimizing signal distortion, excess mean square error, and misadjustment factor compared to the EDNSS algorithm.
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®.
A review of literature shows that there is a variety of adaptive filters. In this research study, we propose a new type of an adaptive filter that increases the diversification used to compensate the chan-nel distortion effect in the MC-CDMA transmission. First, we show the expressions of the filter’s impulse responses in the case of a perfect channel. The adaptive filter has been simulated and experienced by blind equalization for different cases of Gaussian white noise in the case of an MC-CDMA transmission with orthogonal frequency baseband for a mobile radio downlink channel Bran A. The simulation results show the performance of the proposed identification and blind equalization algorithm for MC-CDMA transmission chain using IFFT.
This document summarizes a study on analyzing the impact of impulse noise on OFDM systems using three adaptive algorithms: LMS, NLMS, and RLS. It first describes OFDM systems and impulse noise modeling. It then provides details on the three algorithms - LMS uses a least mean square approach, NLMS is a normalized version of LMS, and RLS uses a recursive least squares approach. Simulation results show transmitted OFDM signals and spectra, as well as BER plots for the different algorithms under varying SNR levels. RLS is found to have the best performance with minimum BER, followed by NLMS, and then LMS. The document concludes RLS is the best algorithm to use for its sustainability to higher
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.
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
This document summarizes a research paper that proposes a new approach for speech recognition using kernel adaptive filtering for speech enhancement and a hybrid HMM/DTW method for recognition. It first discusses adaptive filters and the LMS algorithm, then introduces kernel adaptive filters using the KLMS algorithm to transform input data into a high-dimensional feature space. Finally, it describes using HMM to train speech features and DTW for classification and recognition. The experimental results showed an improvement in recognition rates compared to traditional methods.
The document provides an introduction to adaptive filters, which are computational devices that model the relationship between input and output signals in real time to minimize the error between the actual and desired response. It describes the basic elements of adaptive filters including input/output signals, filter structure, coefficients, and adaptive algorithm. It also summarizes common adaptive filter structures like FIR, IIR, and linear combiners and applications such as system identification, inverse modeling, signal prediction, and interference cancellation.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
This document compares the performance of LMS and RLS adaptive FIR filters for noise cancellation. It finds that as simulation time increases, the LMS filter more effectively removes noise from signals compared to the RLS filter. The LMS filter converges faster but the RLS filter provides better noise reduction, though not complete removal even at long simulation times. The LMS filter requires less complexity and is better for hardware implementations.
DESIGN OF QUATERNARY LOGICAL CIRCUIT USING VOLTAGE AND CURRENT MODE LOGICVLSICS Design
In VLSI technology, designers main concentration were on area required and on performance of the
device. In VLSI design power consumption is one of the major concerns due to continuous increase in chip
density and decline in size of CMOS circuits and frequency at which circuits are operating. By considering
these parameter logical circuits are designed using quaternary voltage mode logic and quaternary current
mode logic. Power consumption required for quaternary voltage mode logic is 51.78 % less as compared
to binary . Area in terms of number of transistor required for quaternary voltage mode logic is 3 times
more as compared to binary. As quaternary voltage mode circuit required large area as compared to
quaternary current mode circuit but power consumption required in quaternary voltage mode circuit is less
than that required in quaternary current mode circuit .
Design of an Adaptive Hearing Aid Algorithm using Booth-Wallace Tree MultiplierWaqas Tariq
The paper presents FPGA implementation of a spectral sharpening process suitable for speech enhancement and noise reduction algorithms for digital hearing aids. Booth and Booth Wallace multiplier is used for implementing digital signal processing algorithms in hearing aids. VHDL simulation results confirm that Booth Wallace multiplier is hardware efficient and performs faster than Booth’s multiplier. Booth Wallace multiplier consumes 40% less power compared to Booth multiplier. A novel digital hearing aid using spectral sharpening filter employing booth Wallace multiplier is proposed. The results reveal that the hardware requirement for implementing hearing aid using Booth Wallace multiplier is less when compared with that of a booth multiplier. Furthermore it is also demonstrated that digital hearing aid using Booth Wallace multiplier consumes less power and performs better in terms of speed.
This document provides an overview of adaptive filters, including:
- Adaptive filters have coefficients that are adjusted based on input data to optimize performance, unlike fixed filters.
- The LMS algorithm is commonly used to adjust coefficients to minimize the mean square error between the filter output and a desired signal.
- Key applications of adaptive filters include noise cancellation, system identification, channel equalization, and echo cancellation.
NOISE CANCELLATION USING LMS ALGORITHM
OBJECTIVE
• INTRODUCTION
• ADAPTIVE FILTER
• BLOCK DIAGRAM
• LEAST MEAN SQUARE - LMS
• ADVANTAGES AND DISADVANTAGES
• MATLAB CODE
• CONCLUSION
ADAPTIVE NOISE CANCELLATION
➢ Adaptive noise cancellation is the approach used for estimating a desired
signal d(n) from a noise-corrupted observation.
x(n) = d(n) + v1(n)
➢ Usually the method uses a primary input containing the corrupted signal
and a reference input containing noise correlated in some unknown way
with the primary noise.
➢ The reference input v1(n) can be filtered and subtracted from the primary
input to obtain the signal estimate 𝑑 ̂(n).
➢ As the measurement system is a black box, no reference signal that is
correlated with the noise is available.
An adaptive filter is composed of two parts, the digital filter and the
adaptive algorithm.
• A digital filter with adjustable coefficients wn(z) and an adaptive algorithm
which is used to adjust or modify the coefficients of the filter.
• The adaptive filter can be a Finite Impulse Response FIR filter or an
Infinite Impulse Response IIR filter.
ALGORITHMS FOR ADAPTIVE EQUALIZATION
• There are three different types of adaptive filtering algorithms.
➢ Zero forcing (ZF)
➢ least mean square (LMS)
➢ Recursive least square filter (RLS)
• Recursive least square is an adaptive filter algorithm that recursively finds the coefficients
that minimize a weighted linear least squares cost function relating to the input signals.
• This approach is different from the least mean-square algorithm that aim to reduce the
mean-square error.
Least Mean Square - LMS
• The LMS algorithm in general, consists of two basics procedure:
1. Filtering process, which involve, computing the output (d(n - d)) of a linear filter in
response to the input signal and generating an estimation error by comparing this
output with a desired response as follows:
y(n) is filter output and is the desired response at time n
2. Adaptive process, which involves the automatics adjustment of the parameter of the
filter in accordance with the estimation error.
➢ where wn is the estimate of the weight value vector at time n, x(n) is the input
signal vector.
➢ e(n) is the filter error vector and μ is the step-size, which determines the filter
convergence rate and overall behavior.
➢ One of the difficulties in the design and implementation of the LMS adaptive
filter is the selection of the step-size μ. This parameter must lie in a specific
range, so that the LMS algorithm converges.
➢ LMS algorithm, aims to reduce the mean-square error.
The convergence characteristics of the LMS adaptive algorithm depends on two
factors: the step-size μ and the eigenvalue spread of the autocorrelation matrix .
The step-size μ must lie in a specific range
where 𝜆𝑚𝑎𝑥 is the largest eigenvalue of the autocorrelation matrix Rx.
• A large value of the step-size μ will lead to a faster convergence but may be less
stable around the minimum value. T
This document discusses real-time digital signal processing and adaptive filters. It covers topics such as conventional filters with fixed coefficients versus adaptive filters with time-varying coefficients. It also discusses random processes, the parts of an adaptive filter including the digital filter and adaptive algorithm, performance functions such as mean-square error, and gradient-based algorithms like the LMS algorithm. Finally, it provides examples of applications for adaptive filters in areas like system identification, noise cancellation, and channel equalization.
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive digital filters and their application in noise cancellation. An adaptive filter uses an algorithm to adjust its transfer function to minimize error and remove correlated noise from a measured signal. The document then discusses using a neural network approach with an exact random basis function for adaptive noise cancellation. It describes the radial basis function network architecture, which has a hidden layer of neurons that respond based on the distance between input and stored patterns. This neural network approach is used to minimize error and obtain an output signal that is closer to the desired input signal with noise removed. Simulation results are also mentioned to demonstrate reduced noise using this neural network algorithm.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive filters and their use in noise cancellation applications. An adaptive noise cancellation system structure is shown using an adaptive filter to estimate noise from a reference input and subtract it from the noisy primary input. Neural networks can be used for adaptive filtering, with the exact random basis function (RBF) network presented as a suitable architecture. Simulation results show that the RBF network achieves much lower error than a linear layer function by producing an output signal close to the desired target. The paper concludes the RBF network is well-suited for this application as it minimizes the error between the output and target signals, effectively canceling linear noise
This document discusses adaptive filtering techniques, specifically the Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms. It describes the basic structure and operation of adaptive filters, including their use of error signals as feedback to optimize transfer functions. The LMS algorithm is commonly used due to its computational simplicity, while RLS provides faster convergence but with higher complexity. The document proposes a modified Delayed LMS (DLMS) adaptive filter architecture to reduce adaptation delay by feeding error computations forward through pipeline stages. Simulation results show this DLMS design achieves lower area, delay and power compared to conventional LMS and RLS filters.
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
Performance Variation of LMS And Its Different VariantsCSCJournals
The document discusses performance variations of different variants of the LMS adaptive filtering algorithm for applications like acoustic echo cancellation. It introduces the LMS algorithm and describes how it works to minimize the mean squared error between the actual and desired signals. Several variants of LMS are also introduced, including NLMS, SLMS, and others. Graphs are shown comparing the performance of LMS and NLMS based on choice of step size, with smaller or larger step sizes impacting convergence speed and error. In conclusion, the choice of step size is very important for the variants of LMS algorithms and affects their performance.
This document discusses using decision feedback equalization to enhance the performance of optical communication systems. It proposes using a fractionally spaced decision feedback equalizer (FSDFE) combined with activity detection guidance (ADG) and tap decoupling (TD) to improve the equalizer's effectiveness. The FSDFE replaces the symbol spaced feedback filter with a fractionally spaced feedback filter to enhance stability, steady-state error performance, and convergence rate. Adding ADG and TD further improves the steady-state error performance and convergence rate by detecting active taps in the channel impulse response. Simulation results show the FSDFE with ADG and TD offers superior performance to the FSDFE without these techniques, with improved compensation of amplitude distortion.
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Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalRPeter Gallagher
In this session delivered at NDC Oslo 2024, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub
This document describes a study that introduces a Modified Error Data Normalized Step Size (MEDNSS) algorithm for an adaptive noise canceller. The MEDNSS algorithm uses a time-varying step size that depends on normalization of both the error and data vectors. The performance of the MEDNSS algorithm is analyzed through computer simulation and compared to the Error Data Normalized Step Size algorithm in stationary and non-stationary environments with different noise power levels. Simulation results show the MEDNSS algorithm significantly improves minimizing signal distortion, excess mean square error, and misadjustment factor compared to the EDNSS algorithm.
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®.
A review of literature shows that there is a variety of adaptive filters. In this research study, we propose a new type of an adaptive filter that increases the diversification used to compensate the chan-nel distortion effect in the MC-CDMA transmission. First, we show the expressions of the filter’s impulse responses in the case of a perfect channel. The adaptive filter has been simulated and experienced by blind equalization for different cases of Gaussian white noise in the case of an MC-CDMA transmission with orthogonal frequency baseband for a mobile radio downlink channel Bran A. The simulation results show the performance of the proposed identification and blind equalization algorithm for MC-CDMA transmission chain using IFFT.
This document summarizes a study on analyzing the impact of impulse noise on OFDM systems using three adaptive algorithms: LMS, NLMS, and RLS. It first describes OFDM systems and impulse noise modeling. It then provides details on the three algorithms - LMS uses a least mean square approach, NLMS is a normalized version of LMS, and RLS uses a recursive least squares approach. Simulation results show transmitted OFDM signals and spectra, as well as BER plots for the different algorithms under varying SNR levels. RLS is found to have the best performance with minimum BER, followed by NLMS, and then LMS. The document concludes RLS is the best algorithm to use for its sustainability to higher
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.
Hybrid hmmdtw based speech recognition with kernel adaptive filtering methodijcsa
This document summarizes a research paper that proposes a new approach for speech recognition using kernel adaptive filtering for speech enhancement and a hybrid HMM/DTW method for recognition. It first discusses adaptive filters and the LMS algorithm, then introduces kernel adaptive filters using the KLMS algorithm to transform input data into a high-dimensional feature space. Finally, it describes using HMM to train speech features and DTW for classification and recognition. The experimental results showed an improvement in recognition rates compared to traditional methods.
The document provides an introduction to adaptive filters, which are computational devices that model the relationship between input and output signals in real time to minimize the error between the actual and desired response. It describes the basic elements of adaptive filters including input/output signals, filter structure, coefficients, and adaptive algorithm. It also summarizes common adaptive filter structures like FIR, IIR, and linear combiners and applications such as system identification, inverse modeling, signal prediction, and interference cancellation.
PERFORMANCE ANALYIS OF LMS ADAPTIVE FIR FILTER AND RLS ADAPTIVE FIR FILTER FO...sipij
This document compares the performance of LMS and RLS adaptive FIR filters for noise cancellation. It finds that as simulation time increases, the LMS filter more effectively removes noise from signals compared to the RLS filter. The LMS filter converges faster but the RLS filter provides better noise reduction, though not complete removal even at long simulation times. The LMS filter requires less complexity and is better for hardware implementations.
DESIGN OF QUATERNARY LOGICAL CIRCUIT USING VOLTAGE AND CURRENT MODE LOGICVLSICS Design
In VLSI technology, designers main concentration were on area required and on performance of the
device. In VLSI design power consumption is one of the major concerns due to continuous increase in chip
density and decline in size of CMOS circuits and frequency at which circuits are operating. By considering
these parameter logical circuits are designed using quaternary voltage mode logic and quaternary current
mode logic. Power consumption required for quaternary voltage mode logic is 51.78 % less as compared
to binary . Area in terms of number of transistor required for quaternary voltage mode logic is 3 times
more as compared to binary. As quaternary voltage mode circuit required large area as compared to
quaternary current mode circuit but power consumption required in quaternary voltage mode circuit is less
than that required in quaternary current mode circuit .
Design of an Adaptive Hearing Aid Algorithm using Booth-Wallace Tree MultiplierWaqas Tariq
The paper presents FPGA implementation of a spectral sharpening process suitable for speech enhancement and noise reduction algorithms for digital hearing aids. Booth and Booth Wallace multiplier is used for implementing digital signal processing algorithms in hearing aids. VHDL simulation results confirm that Booth Wallace multiplier is hardware efficient and performs faster than Booth’s multiplier. Booth Wallace multiplier consumes 40% less power compared to Booth multiplier. A novel digital hearing aid using spectral sharpening filter employing booth Wallace multiplier is proposed. The results reveal that the hardware requirement for implementing hearing aid using Booth Wallace multiplier is less when compared with that of a booth multiplier. Furthermore it is also demonstrated that digital hearing aid using Booth Wallace multiplier consumes less power and performs better in terms of speed.
This document provides an overview of adaptive filters, including:
- Adaptive filters have coefficients that are adjusted based on input data to optimize performance, unlike fixed filters.
- The LMS algorithm is commonly used to adjust coefficients to minimize the mean square error between the filter output and a desired signal.
- Key applications of adaptive filters include noise cancellation, system identification, channel equalization, and echo cancellation.
NOISE CANCELLATION USING LMS ALGORITHM
OBJECTIVE
• INTRODUCTION
• ADAPTIVE FILTER
• BLOCK DIAGRAM
• LEAST MEAN SQUARE - LMS
• ADVANTAGES AND DISADVANTAGES
• MATLAB CODE
• CONCLUSION
ADAPTIVE NOISE CANCELLATION
➢ Adaptive noise cancellation is the approach used for estimating a desired
signal d(n) from a noise-corrupted observation.
x(n) = d(n) + v1(n)
➢ Usually the method uses a primary input containing the corrupted signal
and a reference input containing noise correlated in some unknown way
with the primary noise.
➢ The reference input v1(n) can be filtered and subtracted from the primary
input to obtain the signal estimate 𝑑 ̂(n).
➢ As the measurement system is a black box, no reference signal that is
correlated with the noise is available.
An adaptive filter is composed of two parts, the digital filter and the
adaptive algorithm.
• A digital filter with adjustable coefficients wn(z) and an adaptive algorithm
which is used to adjust or modify the coefficients of the filter.
• The adaptive filter can be a Finite Impulse Response FIR filter or an
Infinite Impulse Response IIR filter.
ALGORITHMS FOR ADAPTIVE EQUALIZATION
• There are three different types of adaptive filtering algorithms.
➢ Zero forcing (ZF)
➢ least mean square (LMS)
➢ Recursive least square filter (RLS)
• Recursive least square is an adaptive filter algorithm that recursively finds the coefficients
that minimize a weighted linear least squares cost function relating to the input signals.
• This approach is different from the least mean-square algorithm that aim to reduce the
mean-square error.
Least Mean Square - LMS
• The LMS algorithm in general, consists of two basics procedure:
1. Filtering process, which involve, computing the output (d(n - d)) of a linear filter in
response to the input signal and generating an estimation error by comparing this
output with a desired response as follows:
y(n) is filter output and is the desired response at time n
2. Adaptive process, which involves the automatics adjustment of the parameter of the
filter in accordance with the estimation error.
➢ where wn is the estimate of the weight value vector at time n, x(n) is the input
signal vector.
➢ e(n) is the filter error vector and μ is the step-size, which determines the filter
convergence rate and overall behavior.
➢ One of the difficulties in the design and implementation of the LMS adaptive
filter is the selection of the step-size μ. This parameter must lie in a specific
range, so that the LMS algorithm converges.
➢ LMS algorithm, aims to reduce the mean-square error.
The convergence characteristics of the LMS adaptive algorithm depends on two
factors: the step-size μ and the eigenvalue spread of the autocorrelation matrix .
The step-size μ must lie in a specific range
where 𝜆𝑚𝑎𝑥 is the largest eigenvalue of the autocorrelation matrix Rx.
• A large value of the step-size μ will lead to a faster convergence but may be less
stable around the minimum value. T
This document discusses real-time digital signal processing and adaptive filters. It covers topics such as conventional filters with fixed coefficients versus adaptive filters with time-varying coefficients. It also discusses random processes, the parts of an adaptive filter including the digital filter and adaptive algorithm, performance functions such as mean-square error, and gradient-based algorithms like the LMS algorithm. Finally, it provides examples of applications for adaptive filters in areas like system identification, noise cancellation, and channel equalization.
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive digital filters and their application in noise cancellation. An adaptive filter uses an algorithm to adjust its transfer function to minimize error and remove correlated noise from a measured signal. The document then discusses using a neural network approach with an exact random basis function for adaptive noise cancellation. It describes the radial basis function network architecture, which has a hidden layer of neurons that respond based on the distance between input and stored patterns. This neural network approach is used to minimize error and obtain an output signal that is closer to the desired input signal with noise removed. Simulation results are also mentioned to demonstrate reduced noise using this neural network algorithm.
Adaptive Digital Filter Design for Linear Noise Cancellation Using Neural Net...iosrjce
This document discusses using neural networks for adaptive digital filter design to cancel linear noise. It begins by introducing adaptive filters and their use in noise cancellation applications. An adaptive noise cancellation system structure is shown using an adaptive filter to estimate noise from a reference input and subtract it from the noisy primary input. Neural networks can be used for adaptive filtering, with the exact random basis function (RBF) network presented as a suitable architecture. Simulation results show that the RBF network achieves much lower error than a linear layer function by producing an output signal close to the desired target. The paper concludes the RBF network is well-suited for this application as it minimizes the error between the output and target signals, effectively canceling linear noise
This document discusses adaptive filtering techniques, specifically the Least Mean Square (LMS) and Recursive Least Squares (RLS) algorithms. It describes the basic structure and operation of adaptive filters, including their use of error signals as feedback to optimize transfer functions. The LMS algorithm is commonly used due to its computational simplicity, while RLS provides faster convergence but with higher complexity. The document proposes a modified Delayed LMS (DLMS) adaptive filter architecture to reduce adaptation delay by feeding error computations forward through pipeline stages. Simulation results show this DLMS design achieves lower area, delay and power compared to conventional LMS and RLS filters.
Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
Performance Variation of LMS And Its Different VariantsCSCJournals
The document discusses performance variations of different variants of the LMS adaptive filtering algorithm for applications like acoustic echo cancellation. It introduces the LMS algorithm and describes how it works to minimize the mean squared error between the actual and desired signals. Several variants of LMS are also introduced, including NLMS, SLMS, and others. Graphs are shown comparing the performance of LMS and NLMS based on choice of step size, with smaller or larger step sizes impacting convergence speed and error. In conclusion, the choice of step size is very important for the variants of LMS algorithms and affects their performance.
This document discusses using decision feedback equalization to enhance the performance of optical communication systems. It proposes using a fractionally spaced decision feedback equalizer (FSDFE) combined with activity detection guidance (ADG) and tap decoupling (TD) to improve the equalizer's effectiveness. The FSDFE replaces the symbol spaced feedback filter with a fractionally spaced feedback filter to enhance stability, steady-state error performance, and convergence rate. Adding ADG and TD further improves the steady-state error performance and convergence rate by detecting active taps in the channel impulse response. Simulation results show the FSDFE with ADG and TD offers superior performance to the FSDFE without these techniques, with improved compensation of amplitude distortion.
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Building a Raspberry Pi Robot with Dot NET 8, Blazor and SignalRPeter Gallagher
In this session delivered at NDC Oslo 2024, I talk about how you can control a 3D printed Robot Arm with a Raspberry Pi, .NET 8, Blazor and SignalR.
I also show how you can use a Unity app on an Meta Quest 3 to control the arm VR too.
You can find the GitHub repo and workshop instructions here;
https://bit.ly/dotnetrobotgithub