Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the
release
function unlocks them.Learning curve Ee2(n) for LMS algorithm time step n µ=0.075 µ=0.025 µ=0.0075 0 500 1000 1500 2000 2500 10−3 10−2 10−1 100 101 Learning curve Ee2(n) for Sign LMS algorithm time step n µ=0.075 µ=0.025 µ=0.0075 µ=0.0025 Sign LMS algorithm should be operated at smaller step-sizes to get a similar behavior as standard LMS algorithm.
Apr 23, 2016 adaptive filter theory by simon haykin pdf free download. Lms filter algorithm, lms filter matlab code, lms filter explained. Lms adaptive filter matlab code, lms adaptive filter verilog code. Simulation of Active Noise Control Using the Filtered-X LMS Algorithm Here we simulate the active noise control system. To emphasize the difference we run the system with no active noise control for the first 200 iterations.
If a property is tunable, you can change its value at any time.
For more information on changing property values, see System Design in MATLAB Using System Objects (MATLAB).
Method
— Method to calculate filter weights
'LMS'
(default) | 'Normalized LMS'
| 'Sign-Data LMS'
| 'Sign-Error LMS'
| 'Sign-Sign LMS'
Method to calculate filter weights, specified as one of the following:
'LMS'
'Normalized LMS'
'Sign-Data LMS'
'Sign-Error LMS'
'Sign-Sign LMS'
For more details on the algorithms, see Algorithms.
Length
— Length of FIR filter weights vector
32
(default) | positive integer
Length of the FIR filter weights vector, specified as a positive integer.
Example: 64
Example: 16
Data Types:
single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
StepSizeSource
— Method to specify adaptation step size
'Property'
(default) | 'Input port'
Method to specify the adaptation step size, specified as one of the following:
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'Property'
–– The property StepSize specifies the size of each adaptation step.'Input port'
–– Specify the adaptation step size as one of the inputs to the object.
StepSize
— Adaptation step size
0.1
(default) | non-negative scalar
Adaptation step size factor, specified as a non-negative scalar. For convergence of the normalized LMS method, the step size must be greater than 0 and less than 2.
A small step size ensures a small steady state error between the output y and the desired signal d. If the step size is small, the convergence speed of the filter decreases. To improve the convergence speed, increase the step size. Note that if the step size is large, the filter can become unstable. To compute the maximum step size the filter can accept without becoming unstable, use the
maxstep
function.Tunable: Yes
Dependencies
This property applies when you set StepSizeSource to
'Property'
.Data Types:
single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
LeakageFactor
— Leakage factor used in leaky LMS method
1
(default) | [0 1]
Leakage factor used when implementing the leaky LMS method, specified as a scalar in the range
[0 1]
. When the value equals 1, there is no leakage in the adapting method. When the value is less than 1, the filter implements a leaky LMS method.Example: 0.5
Tunable: Yes
Data Types:
single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
InitialConditions
— Initial conditions of filter weights
0
(default) | scalar | vector
Initial conditions of filter weights, specified as a scalar or a vector of length equal to the value of the Length property. When the input is real, the value of this property must be real.
Example: 0
Example: [1 3 1 2 7 8 9 0 2 2 8 2]
Data Types:
Complex Number Support: Yes
single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
Complex Number Support: Yes
AdaptInputPort
— Flag to adapt filter weights
false
(default) | true
Flag to adapt filter weights, specified as one of the following:
false
–– The object continuously updates the filter weights.true
–– An adaptation control input is provided to the object when you call its algorithm. If the value of this input is non-zero, the object continuously updates the filter weights. If the value of this input is zero, the filter weights remain at their current value.
WeightsResetInputPort
— Flag to reset filter weights
false
(default) | true
Flag to reset filter weights, specified as one of the following:
false
–– The object does not reset weights.true
–– A reset control input is provided to the object when you call its algorithm. This setting enables the WeightsResetCondition property. The object resets the filter weights based on the values of theWeightsResetCondition
property and the reset input provided to the object algorithm.
WeightsResetCondition
— Event to reset filter weights
'Non-zero'
(default) | 'Rising edge'
| 'Falling edge'
| 'Either edge'
Event that triggers the reset of the filter weights, specified as one of the following. The object resets the filter weights whenever a reset event is detected in its reset input.
'Non-zero'
–– Triggers a reset operation at each sample, when the reset input is not zero.'Rising edge'
–– Triggers a reset operation when the reset input does one of the following:- Rises from a negative value to either a positive value or zero.
- Rises from zero to a positive value, where the rise is not a continuation of a rise from a negative value to zero.
'Falling edge'
–– Triggers a reset operation when the reset input does one of the following:- Falls from a positive value to a negative value or zero.
- Falls from zero to a negative value, where the fall is not a continuation of a fall from a positive value to zero.
'Either edge'
–– Triggers a reset operation when the reset input is a rising edge or a falling edge.
The object resets the filter weights based on the value of this property and the reset input
r
provided to the object algorithm. Dependencies
This property applies when you set the WeightsResetInputPort property to
true
.WeightsOutput
— Method to output adapted filter weights
'Last'
(default) | 'None'
| 'All'
Method to output adapted filter weights, specified as one of the following:
'Last'
(default) — The object returns a column vector of weights corresponding to the last sample of the data frame. The length of the weights vector is the value given by the Length property.'All'
— The object returns a FrameLength-by-Length matrix of weights. The matrix corresponds to the full sample-by-sample history of weights for all FrameLength samples of the input values. Each row in the matrix corresponds to a set of LMS filter weights calculated for the corresponding input sample.'None'
— This setting disables the weights output.
Fixed-Point Properties
RoundingMethod
— Rounding method for fixed-point operations
'Floor'
(default) | 'Ceiling'
| 'Convergent'
| 'Nearest'
| 'Round'
| 'Simplest'
| 'Zero'
Ai a hope on the concrete rarest tree. Specify the rounding mode for fixed-point operations. 1955 chevy on line manual pages. For more details, see rounding mode.
OverflowAction
— Overflow action for fixed-point operations
'Wrap'
(default) | 'Saturate'
Overflow action for fixed-point operations, specified as one of the following:
'Wrap'
–– The object wraps the result of its fixed-point operations.'Saturate'
–– The object saturates the result of its fixed-point operations.
For more details on overflow actions, see overflow mode for fixed-point operations.
StepSizeDataType
— Step size word length and fraction length settings
'Same word length as first input'
(default) | 'Custom'
Step size word length and fraction length settings, specified as one of the following:
'Same word length as first input'
–– The object specifies the word length of step size to be the same as that of the first input. The fraction length is computed to get the best possible precision.'Custom'
–– The step size data type is specified as a custom numeric type through the CustomStepSizeDataType property.
For more information on the step size data type this object uses, see the Fixed Point section.
CustomStepSizeDataType
— Word and fraction lengths of step size
numerictype([],16,15)
(default)
Word and fraction lengths of the step size, specified as an autosigned numeric type with a word length of 16 and a fraction length of 15.
Example: numerictype([],32)
Dependencies
This property applies under the following conditions:
- StepSizeSource property set to
'Property'
and StepSizeDataType set to'Custom'
. StepSizeSource
property set to'Input port'
.
LeakageFactorDataType
— Leakage factor word length and fraction length settings
'Same word length as first input'
(default) | 'Custom'
Leakage factor word length and fraction length settings, specified as one of the following:
'Same word length as first input'
–– The object specifies the word length of leakage factor to be the same as that of the first input. The fraction length is computed to get the best possible precision.'Custom'
–– The leakage factor data type is specified as a custom numeric type through the CustomLeakageFactorDataType property.
For more information on the leakage factor data type this object uses, see the Fixed Point section.
CustomLeakageFactorDataType
— Word and fraction lengths of the leakage factor
numerictype([],16,15)
(default)
Word and fraction lengths of the leakage factor, specified as an autosigned numeric type with a word length of 16 and a fraction length of 15.
Example: numerictype([],32)
Dependencies
This property applies when you set the LeakageFactorDataType property to
'Custom'
.WeightsDataType
— Weights word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Weights word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the filter weights to be the same as that of the first input.'Custom'
–– The data type of filter weights is specified as a custom numeric type through the CustomWeightsDataType property.
For more information on the filter weights data type this object uses, see the Fixed Point section.
CustomWeightsDataType
— Word and fraction lengths of filter weights
numerictype([],16,15)
(default)
Word and fraction lengths of the filter weights, specified as an autosigned numeric type with a word length of 16 and a fraction length of 15.
Example: numerictype([],32,20)
Dependencies
This property applies when you set the WeightsDataType property to
'Custom'
.EnergyProductDataType
— Energy product word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Energy product word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the energy product to be the same as that of the first input.'Custom'
–– The data type of the energy product is specified as a custom numeric type through the CustomEnergyProductDataType property.
For more information on the energy product data type this object uses, see the Fixed Point section.
Dependencies
This property applies when you set the Method property to
'Normalized LMS'
.CustomEnergyProductDataType
— Word and fraction lengths of energy product
numerictype([],32,20)
(default)
Word and fraction lengths of the energy product, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the Method property to
'Normalized LMS'
and EnergyProductDataType property to 'Custom'
.EnergyAccumulatorDataType
— Energy accumulator word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Energy accumulator word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the energy accumulator to be the same as that of the first input.'Custom'
–– The data type of the energy accumulator is specified as a custom numeric type through the CustomEnergyAccumulatorDataType property.
For more information on the energy accumulator data type this object uses, see the Fixed Point section.
Dependencies
This property applies when you set the Method property to
'Normalized LMS'
.CustomEnergyAccumulatorDataType
— Word and fraction lengths of energy accumulator
numerictype([],32,20)
(default)
Word and fraction lengths of the energy accumulator, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the Method property to
'Normalized LMS'
and EnergyAccumulatorDataType property to 'Custom'
.ConvolutionProductDataType
— Convolution product word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Convolution product word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the convolution product to be the same as that of the first input.'Custom'
–– The data type of the convolution product is specified as a custom numeric type through the CustomConvolutionProductDataType property.
For more information on the convolution product data type this object uses, see the Fixed Point section.
CustomConvolutionProductDataType
— Word and fraction lengths of convolution product
numerictype([],32,20)
(default)
Word and fraction lengths of the convolution product, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the ConvolutionProductDataType property to
'Custom'
.ConvolutionAccumulatorDataType
— Convolution accumulator word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Convolution accumulator word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the convolution accumulator to be the same as that of the first input.'Custom'
–– The data type of the convolution accumulator is specified as a custom numeric type through the CustomConvolutionAccumulatorDataType property.
For more information on the convolution accumulator data type this object uses, see the Fixed Point section.
CustomConvolutionAccumulatorDataType
— Word and fraction lengths of convolution accumulator
numerictype([],32,20)
(default)
Word and fraction lengths of the convolution accumulator, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the ConvolutionAccumulatorDataType property to
'Custom'
.StepSizeErrorProductDataType
— Step size error product word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Step size error product word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the step size error product to be the same as that of the first input.'Custom'
–– The data type of the step size error product is specified as a custom numeric type through the CustomStepSizeErrorProductDataType property.
For more information on the step size error product data type this object uses, see the Fixed Point section.
CustomStepSizeErrorProductDataType
— Word and fraction lengths of step size error product
numerictype([],32,20)
(default)
Word and fraction lengths of the step size error product, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the StepSizeErrorProductDataType property to
'Custom'
.WeightsUpdateProductDataType
— Filter weights update product word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Word and fraction length settings of the filter weights update product, specified as one of the following:
'Same as first input'
–– The object specifies the data type of the filter weights update product to be the same as that of the first input.'Custom'
–– The data type of the filter weights update product is specified as a custom numeric type through the CustomWeightsUpdateProductDataType property.
For more information on the filter weights update product data type this object uses, see the Fixed Point section.
CustomWeightsUpdateProductDataType
— Word and fraction lengths of filter weights update product
numerictype([],32,20)
(default)
Word and fraction lengths of the filter weights update product, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the WeightsUpdateProductDataType property to
'Custom'
.QuotientDataType
— Quotient word length and fraction length settings
'Same as first input'
(default) | 'Custom'
Quotient word length and fraction length settings, specified as one of the following:
'Same as first input'
–– The object specifies the quotient data type to be the same as that of the first input.'Custom'
–– The quotient data type is specified as a custom numeric type through the CustomQuotientDataType property.
For more information on the quotient data type this object uses, see the Fixed Point section.
Dependencies
This property applies when you set the Method property to
'Normalized LMS'
.CustomQuotientDataType
— Word and fraction lengths of quotient
numerictype([],32,20)
(default)
Word and fraction lengths of the filter weights update product, specified as an autosigned numeric type with a word length of 32 and a fraction length of 20.
Dependencies
This property applies when you set the Method property to
'Normalized LMS'
and QuotientDataType property to 'Custom'
.Acoustic Noise Cancellation (LMS)
This example shows how to use the Least Mean Square (LMS) algorithm to subtract noise from an input signal. The LMS adaptive filter uses the reference signal on the
Input
port and the desired signal on the Desired
port to automatically match the filter response. As it converges to the correct filter model, the filtered noise is subtracted and the error signal should contain only the original signal.Exploring the Example
In the model, the signal output at the upper port of the Acoustic Environment subsystem is white noise. The signal output at the lower port is composed of colored noise and a signal from a .wav file. This example model uses an adaptive filter to remove the noise from the signal output at the lower port. When you run the simulation, you hear both noise and a person playing the drums. Over time, the adaptive filter in the model filters out the noise so you only hear the drums.
Acoustic Noise Canceler Model
The all-platform floating-point version of the model is shown below.
Utilizing Your Audio Device
By running this model, we can listen to the audio signal in real time (while running the simulation). The stop time is set to infinity. This allows us to interact with the model while it is running. For example, we can change the filter or alternate from slow adaptation to fast adaptation (and vice versa), and get a sense of the real-time audio processing behavior under these conditions.
Color Codes of the Blocks
Notice the colors of the blocks in the model. These are sample time colors that indicate how fast a block executes. Here, the fastest discrete sample time (e.g., the 8 kHz audio signal processing portion) is red, and the second fastest discrete sample time is green. You can see that the color changes from red to green after down-sampling by 32 (in the Downsample block before the Waterfall Scope block). Further information on displaying sample time colors can be found in the Simulink® documentation.
Waterfall Scope
The Waterfall window displays the behavior of the adaptive filter's filter coefficients. It displays multiple vectors of data at one time. These vectors represent the values of the filter's coefficients of a normalized LMS adaptive filter, and are the input data at consecutive sample times. The data is displayed in a three-dimensional axis in the Waterfall window. By default, the x-axis represents amplitude, the y-axis represents samples, and the z-axis represents time. The Waterfall window has toolbar buttons that enable you to zoom in on displayed data, suspend data capture, freeze the scope's display, save the scope position, and export data to the workspace.
Acoustic Environment Subsystem
Lms Algorithm Matlab
You can see the details of the Acoustic Environment subsystem by double clicking on that block. Gaussian noise is used to create the signal sent to the Exterior Mic output port. If the input to the Filter port changes from 0 to 1, the Digital Filter block changes from a lowpass filter to a bandpass filter. The filtered noise output from the Digital Filter block is added to the signal coming from a .wav-file to produce the signal sent to the Pilot's Mic output port.
References
Haykin, S., Adaptive Filter Theory, 3rd Ed., Prentice-Hall, 1996.
Lms Algorithm Matlab Code Pdf Free
Available Example Versions
Floating-point version: dspanc
Matlab Algorithm Examples
Fixed-point version: dspanc_fixpt