dsp.MovingVariance

R2026b

Moving variance

Description

The dsp.MovingVariance System object™ computes the moving variance of the input signal along each channel, independently over time. The object uses either the sliding window method or the exponential weighting method to compute the moving variance. In the sliding window method, a window of specified length is moved over the data, sample by sample, and the variance is computed over the data in the window. In the exponential weighting method, the object subtracts each sample of the data from the average, squares the difference, and multiplies the squared result with a weighting factor. The object then computes the variance by adding all the weighted data.

You can make the window length tunable by setting the EnableTunableWindowLength property to true. In this mode, use the TunableWindowLength property to change the window length even after you pass some data to the object and the object is locked. The MaxWindowLength property specifies the maximum allowed window length. (since R2026b)

For more details, see Algorithms.

The dsp.MovingVariance object and the movvar function both compute the moving variance of the input signal. However, the object can process large streams of real-time data and handle system states automatically. The function performs one-time computations on data that is readily available and cannot handle system states. For a comparison between the two, see System Objects vs MATLAB Functions.

To compute the moving variance of the input:

1. Create the dsp.MovingVariance object and set its properties.

2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

Creation

Syntax

MovVar = dsp.MovingVariance

MovVar = dsp.MovingVariance(Len)

MovVar = dsp.MovingVariance(Len,Overlap)

MovVar = dsp.MovingVariance(PropertyName=Value)

Description

MovVar = dsp.MovingVariance returns a moving variance object, MovVar, using the default properties.

MovVar = dsp.MovingVariance(Len) sets the WindowLength property to Len.

MovVar = dsp.MovingVariance(Len,Overlap) sets the WindowLength property to Len and the OverlapLength property to Overlap.

MovVar = dsp.MovingVariance(PropertyName=Value) specifies additional properties using Name=Value arguments. For example, to specify an exponential weighting factor of 0.2, set ForgettingFactor to 0.9

Properties

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.

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.

Method — Method to compute the variance

"Sliding window" (default) | "Exponential weighting"

• "Sliding window" — A window of length specified by SpecifyWindowLength is moved over the input data along each channel. For every sample the window moves by, the object computes the variance over the data in the window.

• "Exponential weighting" — The object subtracts each sample of the data from the average, squares the difference, and multiplies the squared result with a weighting factor. The object then computes the variance by adding all the weighted data. The magnitude of the weighting factors decreases exponentially as the age of the data increases, never reaching zero.

For more details on these methods, see Algorithms.

SpecifyWindowLength — Specify window length

true (default) | false

Flag to specify a window length, specified as a scalar Boolean.

• true — The length of the sliding window is equal to the value you specify in the WindowLength property.

• false — The length of the sliding window is infinite. In this mode, the variance is computed using the current sample and all past samples.

Dependencies

This property applies when you set Method to "Sliding window".

EnableTunableWindowLength — Option to enable tunable window length

false (default) | true

Since R2026b

Option to enable tunable window length, specified as a scalar boolean.

• true — The window length is tunable, that is, you can change its value even after you pass some data to the object and the object is locked. Use the TunableWindowLength property to specify the window length.

• false — The window length is not tunable. Use the WindowLength property to specify a fixed window length.

Dependencies

This property applies when you set:

• Method to "Sliding window"

• SpecifyWindowLength to true

TunableWindowLength — Tunable sliding window length

4 (default) | positive integer in the range [1, MaxWindowLength]

Since R2026b

Tunable sliding window length in samples, specified as a positive integer in the range [1, MaxWindowLength]. You can change the value of this property even when the object is locked.

When you set AutoAdjustInvalidWindowLength to true, the object adjusts invalid values and issues a warning:

• If the tunable window length value is greater than MaxWindowLength, the object uses MaxWindowLength.

• If the tunable window length value is less than 1, the object uses 1.

• If the tunable window length value is not an integer, the object uses the floored value.

When you set AutoAdjustInvalidWindowLength to false, the object throws an error for invalid values.

Tunable: Yes

Dependencies

This property applies when you set:

• Method to "Sliding window"

• SpecifyWindowLength to true

• EnableTunableWindowLength to true

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

MaxWindowLength — Maximum window length

20 (default) | positive integer

Since R2026b

Maximum value of the tunable window length, specified as a positive integer. The TunableWindowLength property must be less than or equal to this value.

Dependencies

This property applies when you set:

• Method to "Sliding window"

• SpecifyWindowLength to true

• EnableTunableWindowLength to true

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

AutoAdjustInvalidWindowLength — Automatically adjust invalid window length

true (default) | false

Since R2026b

Option to automatically adjust invalid window length values, specified as a scalar boolean.

• true — The object adjusts invalid TunableWindowLength values and issues a warning.

  • If the tunable window length value is greater than MaxWindowLength, the object sets its value to MaxWindowLength.

  • If the tunable window length value is less than 1, the object sets its value to 1.

  • If the tunable window length value is not an integer, the object uses the floored value.

• false — The object throws an error when the tunable window length value is invalid.

Dependencies

This property applies when you set:

• Method to "Sliding window"

• SpecifyWindowLength to true

• EnableTunableWindowLength to true

WindowLength — Length of the sliding window

4 (default) | positive integer

Length of the sliding window in samples, specified as a positive integer. You cannot tune the value of this property after the object is locked. To tune the window length, set the EnableTunableWindowLength property to true.

Dependencies

This property applies when you set:

• Method to "Sliding window"

• SpecifyWindowLength to true

• EnableTunableWindowLength to false

(since R2026b)

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

OverlapLength — Overlap length between windows

WindowLength − 1 (default) | nonnegative integer

Overlap length between sliding windows, specified as a nonnegative integer. The value of overlap length varies in the range [0, WindowLength − 1]. If not specified, the overlap length is set to WindowLength − 1.

Dependencies

This property applies when you set:

• Method to "Sliding window"

• SpecifyWindowLength to true

• EnableTunableWindowLength to false

(since R2026b)

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

ForgettingFactor — Exponential weighting factor

0.9 (default) | nonnegative real scalar in the range [0,1]

Exponential weighting factor, specified as a nonnegative real scalar in the range [0,1].

A forgetting factor of 0.9 gives more weight to the older data than does a forgetting factor of 0.1. A forgetting factor of 1.0 indicates infinite memory and all the past samples are given an equal weight. A forgetting factor of 0 indicates no memory and the past samples have no weight on the current computation.

Since this property is tunable, you can change its value even when the object is locked.

Tunable: Yes

Dependencies

This property applies when you set Method to "Exponential weighting".

Data Types: single | double

Usage

Syntax

y = movVar(x)

Description

y = movVar(x) computes the moving variance of the input signal, x, using either the sliding window method or exponential weighting method.

Input Arguments

x — Data input

vector | matrix

Data input, specified as a vector or a matrix. If x is a matrix, each column is treated as an independent channel. The moving variance is computed along each channel.

The object accepts variable-size inputs. Once the object is locked, you can change the size of each input channel, but you cannot change the number of channels.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

y — Moving variance

vector | matrix

Moving variance of the input signal, returned as a vector or a matrix.

When you input a signal of size m-by-n to the object, and if you set Method to "Sliding window" and SpecifyWindowLength to true, the output has an upper bound size of ceil(m/hop size)-by-n. Hop size is window length − overlap length. In other cases, the output has a size of m-by-n.

When you generate code from this object, the variable-size behavior of the output in the generated code depends on the input frame length and whether the size of the input signal is fixed or variable. For more details, see Code Generation.

Data Types: single | double
Complex Number Support: Yes

Object Functions

To use an object function, specify the System object as the first input argument. For example, to release system resources of a System object named obj, use this syntax:

release(obj)

Common to All System Objects

step	Run System object algorithm
release	Release resources and allow changes to System object property values and input characteristics
reset	Reset internal states of System object

Examples

Moving Variance of Noisy Square Wave Signal

Compute the moving variance of a noisy square wave signal with varying amplitude using the dsp.MovingVariance object.

Initialization

Set up movvarWindow, movvarWindow_overlap, and movvarExp objects. movvarWindow uses the sliding window method with a window length of 800 samples and a default overlap length of 799 samples, which is 1 sample less than the specified window length. movvarWindow_overlap uses a window length of 800 samples and an overlap length of 700 samples. movvarExp uses the exponentially weighting method with a forgetting factor of 0.999.

Create a time scope for viewing the output.

FrameLength = 100;
Fs = 100;
movvarWindow = dsp.MovingVariance(800);
movvarWindow_overlap = dsp.MovingVariance(800,700);
movvarExp = dsp.MovingVariance(Method="Exponential weighting",...
    ForgettingFactor=0.999);
scope  = timescope(SampleRate=[Fs,Fs,Fs/(800-700),Fs],...
    TimeSpanOverrunAction="Scroll",...
    TimeSpanSource="Property",...
    TimeSpan=1000,...
    ShowGrid=true,...
    BufferLength=1e7,...
    YLimits=[0 7e-4]);
title = "Moving Variance";
scope.Title = title;
scope.ChannelNames = {"Original Signal",...
    "Sliding window of 800 samples with default overlap",...
    "Sliding window of 800 samples with an overlap of 700 samples",...
    "Exponential weighting with forgetting factor of 0.999"};

Compute the Variance

Generate a noisy square wave signal. Vary the amplitude of the square wave after a given number of frames. Apply the sliding window method and the exponentially weighting method on this signal. The actual variance is np. This value is used while adding noise to the data. Compare the actual variance with the computed variances on the time scope.

count = 1;
noisepower = 1e-4 * [1 2 3 4];
for index = 1:length(noisepower)
    np = noisepower(index);
    yexp = np*ones(FrameLength,1);
    for i = 1:250
        x = 1 + sqrt(np) * randn(FrameLength,1);
        y1 = movvarWindow(x);
        y2 = movvarWindow_overlap(x);
        y3 = movvarExp(x);
        scope(yexp,y1,y2,y3);
    end
end

Adaptive Power Estimation with Tunable Window Length

Estimate the power of a noisy signal whose noise level changes over time. Adapt the variance window length inversely to the noise power so that periods of heavy noise use a shorter window to track rapid changes and periods of light noise use a longer window for a more accurate estimate.

Create Input Signal

Create a sine wave signal with a frequency of 5 Hz. Set the sampling rate to 1000 Hz and the frame size to 256 samples.

Fs = 1000;
frameSize = 256;
numFrames = 40;
sine = dsp.SineWave(Frequency=5,SampleRate=Fs,...
    SamplesPerFrame=frameSize)

sine = 
  dsp.SineWave with properties:

          Amplitude: 1
          Frequency: 5
        PhaseOffset: 0
      ComplexOutput: false
             Method: 'Trigonometric function'
    SamplesPerFrame: 256
         SampleRate: 1000
     OutputDataType: 'double'

Create Moving Variance Object

Create a dsp.MovingVariance object with tunable window length enabled. Set the maximum window length to 50 and the initial tunable window length to 40 (long window for accurate baseline estimate). Enable auto-adjustment to handle edge cases.

movVar = dsp.MovingVariance(EnableTunableWindowLength=true,...
    TunableWindowLength=40,...
    MaxWindowLength=50,...
    AutoAdjustInvalidWindowLength=true)

movVar = 
  dsp.MovingVariance with properties:

                           Method: 'Sliding window'
              SpecifyWindowLength: true
        EnableTunableWindowLength: true
              TunableWindowLength: 40
                  MaxWindowLength: 50
    AutoAdjustInvalidWindowLength: true

Create a timescope object to view the input signal and the estimated power.

scope = timescope(SampleRate=Fs,...
    TimeSpanSource="property",TimeSpan=numFrames*frameSize/Fs,...
    TimeSpanOverrunAction="Scroll",...
    ShowGrid=true,YLimits=[-4 4],ChannelNames={"Noisy Signal","Estimated Power (Moving Variance)"},...
    LayoutDimensions=[2 1],NumInputPorts=2);

Apply Adaptive Power Estimation

The noise power increases linearly over time. At each frame, add Gaussian noise whose power grows from low to high. Adapt the window length inversely to the noise power so that heavier noise is tracked with a shorter window for faster response to changing power levels.

As the noise power increases over time, the adaptive algorithm uses a progressively shorter window to track the rising power more closely.

for index = 1:numFrames
    x = sine();
    noisePower = 0.01 + 0.99*(index/numFrames);
    noisySignal = x + sqrt(noisePower)*randn(frameSize,1);
    wl = round(50 - 45*(index/numFrames));
    movVar.TunableWindowLength = wl;
    y = movVar(noisySignal);
    scope(noisySignal,y)
end

Generate Code for Moving Variance with Tunable Window Length

Generate C code for a moving variance computation with a tunable window length parameter. Verify that the window length appears as a tunable parameter in the generated code.

Create Entry-Point Function

Create an entry-point function that instantiates a dsp.MovingVariance object with tunable window length enabled. The function accepts a data input and a tunable window length value.

type movingVarTunableWL.m

function y = movingVarTunableWL(x,wl) %#codegen
%movingVarTunableWL Moving variance with tunable window length
%   y = movingVarTunableWL(x,wl) computes the moving variance of
%   the input x using a window length that can be changed at runtime.

persistent movVar
if isempty(movVar)
    movVar = dsp.MovingVariance(EnableTunableWindowLength=true,...
        MaxWindowLength=31,...
        AutoAdjustInvalidWindowLength=true);
end

movVar.TunableWindowLength = wl;
y = movVar(x);
end

Generate C Code

Generate C code for the entry-point function using codegen. Specify the input data as a 256-by-1 column vector of doubles and the window length as a scalar double.

codegen movingVarTunableWL -args {zeros(256,1),0} -config:lib -report

Code generation successful: View report

Verify Tunable Parameter in Generated Code

Inspect the generated code to verify that TunableWindowLength appears as a parameter that can be changed at runtime without regenerating code.

The generated code contains the TunableWindowLength field in the persistent object state, confirming that you can change the window length at runtime in the deployed code.

Algorithms

Sliding Window Method

In the sliding window method, the output at the current sample is the variance of the current sample with respect to the data in the window. To compute the first output sample, the algorithm waits until it receives the hop size number of input samples. Hop size is defined as window length – overlap length. Remaining samples in the window are considered to be zero. As an example, if the window length is 5 and the overlap length is 2, then the algorithm waits until it receives 3 samples of input to compute the first sample of the output. After generating the first output, it generates the subsequent output samples for every hop size number of input samples.

When you do not specify the window length, the algorithm chooses an infinite window length. In this mode, the output is the moving variance of the current sample with respect to all previous samples in the channel.

Consider an example of computing the moving variance of a streaming input data using the sliding window method. The algorithm uses a window length of 4 and an overlap length of 3. With each input sample that comes in, the window of length 4 moves along the data.

Exponential Weighting Method

In the exponential weighting method, the moving variance is computed recursively using these formulas:

$$\begin{array}{l}{s}^2{}_{N,\lambda }=\frac{1}{v_{N,\lambda }}{\displaystyle \sum _{k=1}^N{\lambda }^{N-k}}{\left[x_k-{\overline{x}}_{N,\lambda }\right]}^2\\ v_{N,\lambda }=\frac{2\lambda (1-{\lambda }^{N-1})}{(1-\lambda )(1+\lambda )}\end{array}$$

To compute the moving variance, the algorithm implements these equations recursively.

• $$s^2{}_{N,\lambda }$$ — Moving variance of the current data sample with respect to the rest of the data in the channel.

• $${\overline{x}}_{N,\lambda }$$ — Moving average at the current sample. For details on computing the moving average, see dsp.MovingAverage.

• $${\left[x_k-{\overline{x}}_{N,\lambda }\right]}^2$$ — Difference between each data sample and the average of the data, squared.

• $${\displaystyle \sum _{k=1}^N{\lambda }^{N-k}}{\left[x_k-{\overline{x}}_{N,\lambda }\right]}^2$$ — Difference between each data sample and the average of the data, squared and multiplied with the forgetting factor. All the squared terms are added.

• $$\frac{1}{v_{N,\lambda }}$$ — Weighting factor applied to the sum.

• λ — Forgetting factor you can specify through the ForgettingFactor property.

As the age of the data increases, the magnitude of the weighting factor decreases exponentially, and never reaches zero. In other words, the recent data has more influence on the current variance, than the older data.

The value of the forgetting factor determines the rate of change of the weighting factors. A forgetting factor of 0.9 gives more weight to the older data than does a forgetting factor of 0.1. A forgetting factor of 1.0 indicates infinite memory. All the past samples are given an equal weight.

Consider an example of computing the moving variance using the exponential weighting method. The forgetting factor is 0.9.

Tunable Window Length Algorithm

When you specify the window length to be tunable, the moving variance algorithm maintains an internal buffer of size MaxWL−1 to store past input samples, where MaxWL is the maximum window length. At each time step, the moving variance algorithm performs these operations:

1. Concatenates the buffer with the current input to form an extended data sequence.

2. Extracts the most recent window length samples from the extended data sequence.

3. Computes the moving variance over the extracted samples using a sliding window and discards the endpoints.

4. Stores the most recent MaxWL−1 samples in the buffer for the next time step.

This approach allows you to change the window length at each time step while the algorithm maintains continuity of the output signal.

References

[1] Bodenham, Dean. “Adaptive Filtering and Change Detection for Streaming Data.” PH.D. Thesis. Imperial College, London, 2012.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• See System Objects in MATLAB Code Generation (MATLAB Coder).

• When you set Method to "Sliding window" and SpecifyWindowLength to true, and generate code from this object, the variable-size behavior of the output depends on the input frame length and whether the size of the input signal is fixed or variable.

  See this table for more details.

  Input signal	Input size	Output signal
  Fixed-size	m-by-n	When the input frame length is a multiple of the hop size, the output signal has a fixed-size of (m/hop size)-by-n. When input frame length is not a multiple of the hop size, the output signal is variable-sized and has an upper bound of ceil(m/hop size)-by-n.
  Variable-size	m-by-n	Output is a variable-size signal. Output has an upper bound size of ceil(m/hop size)-by-n.

Version History

Introduced in R2016b

R2026b: Tunable window length

You can now change the window length even after the object is locked by setting the EnableTunableWindowLength property to true. Use the TunableWindowLength property to specify the window length. The MaxWindowLength property defines the upper bound for the tunable window length. Use the AutoAdjustInvalidWindowLength property to control whether invalid window length values are automatically adjusted with a warning or cause an error.

R2024b: Specify a forgetting factor of 0

You can now specify a forgetting factor of 0 in the dsp.MovingVariance object.

R2022b: Change in variable-size behavior for output signal in generated code

Starting in R2022b, if you generate code from this object with the Method property set to "Sliding window" and the SpecifyWindowLength property set to true, and if you input a signal with a frame length that is a multiple of the hop size (window length − overlap length), this object generates a fixed-size output signal in the generated code. For more details, see Code Generation.