Welch’s power spectral density estimate (2024)

Welch’s power spectral density estimate

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Syntax

pxx = pwelch(x)

pxx = pwelch(x,window)

pxx = pwelch(x,window,noverlap)

pxx = pwelch(x,window,noverlap,nfft)

[pxx,w] = pwelch(___)

[pxx,f] = pwelch(___,fs)

[pxx,w] = pwelch(x,window,noverlap,w)

[pxx,f] = pwelch(x,window,noverlap,f,fs)

[___] = pwelch(x,window,___,freqrange)

[___] = pwelch(x,window,___,trace)

[___,pxxc] = pwelch(___,'ConfidenceLevel',probability)

[___] = pwelch(___,spectrumtype)

pwelch(___)

Description

example

pxx = pwelch(x) returnsthe power spectral density (PSD) estimate, pxx,of the input signal, x, found using Welch's overlappedsegment averaging estimator. When x is a vector,it is treated as a single channel. When x isa matrix, the PSD is computed independently for each column and storedin the corresponding column of pxx. If x isreal-valued, pxx is a one-sided PSD estimate.If x is complex-valued, pxx isa two-sided PSD estimate. By default, x is dividedinto the longest possible segments to obtain as close to but not exceed8 segments with 50% overlap. Each segment is windowed with a Hammingwindow. The modified periodograms are averaged to obtain the PSD estimate.If you cannot divide the length of x exactlyinto an integer number of segments with 50% overlap, x istruncated accordingly.

example

pxx = pwelch(x,window) usesthe input vector or integer, window, to dividethe signal into segments. If window is a vector, pwelch dividesthe signal into segments equal in length to the length of window.The modified periodograms are computed using the signal segments multipliedby the vector, window. If window isan integer, the signal is divided into segments of length window.The modified periodograms are computed using a Hamming window of length window.

example

pxx = pwelch(x,window,noverlap) uses noverlap samplesof overlap from segment to segment. noverlap mustbe a positive integer smaller than window if window isan integer. noverlap must be a positive integerless than the length of window if window isa vector. If you do not specify noverlap, orspecify noverlap as empty, the default numberof overlapped samples is 50% of the window length.

example

pxx = pwelch(x,window,noverlap,nfft) specifiesthe number of discrete Fourier transform (DFT) points to use in thePSD estimate. The default nfft is the greaterof 256 or the next power of 2 greater than the length of the segments.

[pxx,w] = pwelch(___) returns the normalized frequency vector, w. If pxx is a one-sided PSD estimate, w spans the interval [0,π] if nfft is even and [0,π) if nfft is odd. If pxx is a two-sided PSD estimate, w spans the interval [0,2π).

example

[pxx,f] = pwelch(___,fs) returns a frequency vector, f, in cycles per unit time. The sample rate, fs, is the number of samples per unit time. If the unit of time is seconds, then f is in cycles/sec (Hz). For real–valued signals, f spans the interval [0,fs/2] when nfft is even and [0,fs/2) when nfft is odd. For complex-valued signals, f spans the interval [0,fs). fs must be the fifth input to pwelch. To input a sample rate and still use the default values of the preceding optional arguments, specify these arguments as empty, [].

[pxx,w] = pwelch(x,window,noverlap,w) returns the two-sided Welch PSD estimates at the normalized frequencies specified in the vector, w. The vector w must contain at least two elements, because otherwise the function interprets it as nfft.

[pxx,f] = pwelch(x,window,noverlap,f,fs) returns the two-sided Welch PSD estimates at the frequencies specified in the vector, f. The vector f must contain at least two elements, because otherwise the function interprets it as nfft. The frequencies in f are in cycles per unit time. The sample rate, fs, is the number of samples per unit time. If the unit of time is seconds, then f is in cycles/sec (Hz).

example

[___] = pwelch(x,window,___,freqrange) returnsthe Welch PSD estimate over the frequency range specified by freqrange.Valid options for freqrange are: 'onesided', 'twosided',or 'centered'.

example

[___] = pwelch(x,window,___,trace) returnsthe maximum-hold spectrum estimate if trace isspecified as 'maxhold' and returns the minimum-holdspectrum estimate if trace is specified as 'minhold'.

example

[___,pxxc] = pwelch(___,'ConfidenceLevel',probability) returnsthe probability×100%confidence intervals for the PSD estimate in pxxc.

example

[___] = pwelch(___,spectrumtype) returns the PSD estimate if spectrumtype is specified as 'psd' and returns the power spectrum if spectrumtype is specified as 'power'.

example

pwelch(___) with no outputarguments plots the Welch PSD estimate in the current figure window.

Examples

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Welch Estimate Using Default Inputs

Open Live Script

Obtain the Welch PSD estimate of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/4 rad/sample with additive N(0,1) white noise.

Create a sine wave with an angular frequency of π/4 rad/sample with additive N(0,1) white noise. Reset the random number generator for reproducible results. The signal has a length Nx=320 samples.

rng defaultn = 0:319;x = cos(pi/4*n)+randn(size(n));

Obtain the Welch PSD estimate using the default Hamming window and DFT length. The default segment length is 71 samples and the DFT length is the 256 points yielding a frequency resolution of 2π/256 rad/sample. Because the signal is real-valued, the periodogram is one-sided and there are 256/2+1 points. Plot the Welch PSD estimate.

pxx = pwelch(x);pwelch(x)

Welch’s power spectral density estimate (1)

Repeat the computation.

  • Divide the signal into sections of length nsc=Nx/4.5. This action is equivalent to dividing the signal into the longest possible segments to obtain as close to but not exceed 8 segments with 50% overlap.

  • Window the sections using a Hamming window.

  • Specify 50% overlap between contiguous sections

  • To compute the FFT, use max(256,2p) points, where p=log2nsc.

Verify that the two approaches give identical results.

Nx = length(x);nsc = floor(Nx/4.5);nov = floor(nsc/2);nff = max(256,2^nextpow2(nsc));t = pwelch(x,hamming(nsc),nov,nff);maxerr = max(abs(abs(t(:))-abs(pxx(:))))
maxerr = 0

Divide the signal into 8 sections of equal length, with 50% overlap between sections. Specify the same FFT length as in the preceding step. Compute the Welch PSD estimate and verify that it gives the same result as the previous two procedures.

ns = 8;ov = 0.5;lsc = floor(Nx/(ns-(ns-1)*ov));t = pwelch(x,lsc,floor(ov*lsc),nff);maxerr = max(abs(abs(t(:))-abs(pxx(:))))
maxerr = 0

Welch Estimate Using Specified Segment Length

Open Live Script

Obtain the Welch PSD estimate of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/3 rad/sample with additive N(0,1) white noise.

Create a sine wave with an angular frequency of π/3 rad/sample with additive N(0,1) white noise. Reset the random number generator for reproducible results. The signal has 512 samples.

rng defaultn = 0:511;x = cos(pi/3*n)+randn(size(n));

Obtain the Welch PSD estimate dividing the signal into segments 132 samples in length. The signal segments are multiplied by a Hamming window 132 samples in length. The number of overlapped samples is not specified, so it is set to 132/2 = 66. The DFT length is 256 points, yielding a frequency resolution of 2π/256 rad/sample. Because the signal is real-valued, the PSD estimate is one-sided and there are 256/2+1 = 129 points. Plot the PSD as a function of normalized frequency.

segmentLength = 132;[pxx,w] = pwelch(x,segmentLength);plot(w/pi,10*log10(pxx))xlabel('\omega / \pi')

Welch’s power spectral density estimate (2)

Welch Estimate Specifying Segment Overlap

Open Live Script

Obtain the Welch PSD estimate of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/4 rad/sample with additive N(0,1) white noise.

Create a sine wave with an angular frequency of π/4 rad/sample with additive N(0,1) white noise. Reset the random number generator for reproducible results. The signal is 320 samples in length.

rng defaultn = 0:319;x = cos(pi/4*n)+randn(size(n));

Obtain the Welch PSD estimate dividing the signal into segments 100 samples in length. The signal segments are multiplied by a Hamming window 100 samples in length. The number of overlapped samples is 25. The DFT length is 256 points yielding a frequency resolution of 2π/256 rad/sample. Because the signal is real-valued, the PSD estimate is one-sided and there are 256/2+1 points.

segmentLength = 100;noverlap = 25;pxx = pwelch(x,segmentLength,noverlap);plot(10*log10(pxx))

Welch’s power spectral density estimate (3)

Welch Estimate Using Specified DFT Length

Open Live Script

Obtain the Welch PSD estimate of an input signal consisting of a discrete-time sinusoid with an angular frequency of π/4 rad/sample with additive N(0,1) white noise.

Create a sine wave with an angular frequency of π/4 rad/sample with additive N(0,1) white noise. Reset the random number generator for reproducible results. The signal is 320 samples in length.

rng defaultn = 0:319;x = cos(pi/4*n) + randn(size(n));

Obtain the Welch PSD estimate dividing the signal into segments 100 samples in length. Use the default overlap of 50%. Specify the DFT length to be 640 points so that the frequency of π/4 rad/sample corresponds to a DFT bin (bin 81). Because the signal is real-valued, the PSD estimate is one-sided and there are 640/2+1 points.

segmentLength = 100;nfft = 640;pxx = pwelch(x,segmentLength,[],nfft);plot(10*log10(pxx))xlabel('rad/sample')ylabel('dB / (rad/sample)')

Welch’s power spectral density estimate (4)

Welch PSD Estimate of Signal with Frequency in Hertz

Open Live Script

Create a signal consisting of a 100 Hz sinusoid in additive N(0,1) white noise. Reset the random number generator for reproducible results. The sample rate is 1 kHz and the signal is 5 seconds in duration.

rng defaultfs = 1000;t = 0:1/fs:5-1/fs;x = cos(2*pi*100*t) + randn(size(t));

Obtain Welch's overlapped segment averaging PSD estimate of the preceding signal. Use a segment length of 500 samples with 300 overlapped samples. Use 500 DFT points so that 100 Hz falls directly on a DFT bin. Input the sample rate to output a vector of frequencies in Hz. Plot the result.

[pxx,f] = pwelch(x,500,300,500,fs);plot(f,10*log10(pxx))xlabel('Frequency (Hz)')ylabel('PSD (dB/Hz)')

Maximum-Hold and Minimum-Hold Spectra

Open Live Script

Create a signal consisting of three noisy sinusoids and a chirp, sampled at 200 kHz for 0.1 second. The frequencies of the sinusoids are 1 kHz, 10 kHz, and 20 kHz. The sinusoids have different amplitudes and noise levels. The noiseless chirp has a frequency that starts at 20 kHz and increases linearly to 30 kHz during the sampling.

Fs = 200e3; Fc = [1 10 20]'*1e3; Ns = 0.1*Fs;t = (0:Ns-1)/Fs;x = [1 1/10 10]*sin(2*pi*Fc*t)+[1/200 1/2000 1/20]*randn(3,Ns);x = x+chirp(t,20e3,t(end),30e3);

Compute the Welch PSD estimate and the maximum-hold and minimum-hold spectra of the signal. Plot the results.

[pxx,f] = pwelch(x,[],[],[],Fs);pmax = pwelch(x,[],[],[],Fs,'maxhold');pmin = pwelch(x,[],[],[],Fs,'minhold');plot(f,pow2db(pxx))hold onplot(f,pow2db([pmax pmin]),':')hold offxlabel('Frequency (Hz)')ylabel('PSD (dB/Hz)')legend('pwelch','maxhold','minhold')

Welch’s power spectral density estimate (6)

Repeat the procedure, this time computing centered power spectrum estimates.

[pxx,f] = pwelch(x,[],[],[],Fs,'centered','power');pmax = pwelch(x,[],[],[],Fs,'maxhold','centered','power');pmin = pwelch(x,[],[],[],Fs,'minhold','centered','power');plot(f,pow2db(pxx))hold onplot(f,pow2db([pmax pmin]),':')hold offxlabel('Frequency (Hz)')ylabel('Power (dB)')legend('pwelch','maxhold','minhold')

Welch’s power spectral density estimate (7)

Upper and Lower 95%-Confidence Bounds

Open Live Script

This example illustrates the use of confidence bounds with Welch's overlapped segment averaging (WOSA) PSD estimate. While not a necessary condition for statistical significance, frequencies in Welch's estimate where the lower confidence bound exceeds the upper confidence bound for surrounding PSD estimates clearly indicate significant oscillations in the time series.

Create a signal consisting of the superposition of 100 Hz and 150 Hz sine waves in additive white N(0,1) noise. The amplitude of the two sine waves is 1. The sample rate is 1 kHz. Reset the random number generator for reproducible results.

rng defaultfs = 1000;t = 0:1/fs:1-1/fs;x = cos(2*pi*100*t)+sin(2*pi*150*t)+randn(size(t));

Obtain the WOSA estimate with 95%-confidence bounds. Set the segment length equal to 200 and overlap the segments by 50% (100 samples). Plot the WOSA PSD estimate along with the confidence interval and zoom in on the frequency region of interest near 100 and 150 Hz.

L = 200;noverlap = 100;[pxx,f,pxxc] = pwelch(x,hamming(L),noverlap,200,fs,... 'ConfidenceLevel',0.95);plot(f,10*log10(pxx))hold onplot(f,10*log10(pxxc),'-.')hold offxlim([25 250])xlabel('Frequency (Hz)')ylabel('PSD (dB/Hz)')title('Welch Estimate with 95%-Confidence Bounds')

Welch’s power spectral density estimate (8)

The lower confidence bound in the immediate vicinity of 100 and 150 Hz is significantly above the upper confidence bound outside the vicinity of 100 and 150 Hz.

DC-Centered Power Spectrum

Open Live Script

Create a signal consisting of a 100 Hz sinusoid in additive N(0,1/4) white noise. Reset the random number generator for reproducible results. The sample rate is 1 kHz and the signal is 5 seconds in duration.

rng defaultfs = 1000;t = 0:1/fs:5-1/fs;noisevar = 1/4;x = cos(2*pi*100*t)+sqrt(noisevar)*randn(size(t));

Obtain the DC-centered power spectrum using Welch's method. Use a segment length of 500 samples with 300 overlapped samples and a DFT length of 500 points. Plot the result.

[pxx,f] = pwelch(x,500,300,500,fs,'centered','power');plot(f,10*log10(pxx))xlabel('Frequency (Hz)')ylabel('Magnitude (dB)')grid

Welch’s power spectral density estimate (9)

You see that the power at -100 and 100 Hz is close to the expected power of 1/4 for a real-valued sine wave with an amplitude of 1. The deviation from 1/4 is due to the effect of the additive noise.

Welch PSD Estimate of a Multichannel Signal

Open Live Script

Generate 1024 samples of a multichannel signal consisting of three sinusoids in additive N(0,1) white Gaussian noise. The sinusoids' frequencies are π/2, π/3, and π/4 rad/sample. Estimate the PSD of the signal using Welch's method and plot it.

N = 1024;n = 0:N-1;w = pi./[2;3;4];x = cos(w*n)' + randn(length(n),3);pwelch(x)

Welch’s power spectral density estimate (10)

Input Arguments

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noverlapNumber of overlapped samples
positive integer | []

Number of overlapped samples, specified as a positive integersmaller than the length of window. If you omit noverlap orspecify noverlap as empty, a value is used toobtain 50% overlap between segments.

nfftNumber of DFT points
max(256,2^nextpow2(length(window))) (default) | integer | []

Number of DFT points, specified as a positive integer. For areal-valued input signal, x, the PSD estimate, pxx haslength (nfft/2+1)if nfft is even, and (nfft+1)/2 if nfft isodd. For a complex-valued input signal,x, thePSD estimate always has length nfft. If nfft isspecified as empty, the default nfft is used.

If nfft is greater than the segment length,the data is zero-padded. If nfft is less thanthe segment length, the segment is wrapped using datawrap tomake the length equal to nfft.

Data Types: single | double

traceTrace mode
'mean' (default) | 'maxhold' | 'minhold'

Trace mode, specified as one of 'mean', 'maxhold',or 'minhold'. The default is 'mean'.

  • 'mean' — returns the Welchspectrum estimate of each input channel. pwelch computesthe Welch spectrum estimate at each frequency bin by averaging thepower spectrum estimates of all the segments.

  • 'maxhold' — returns themaximum-hold spectrum of each input channel. pwelch computesthe maximum-hold spectrum at each frequency bin by keeping the maximumvalue among the power spectrum estimates of all the segments.

  • 'minhold' — returns theminimum-hold spectrum of each input channel. pwelch computesthe minimum-hold spectrum at each frequency bin by keeping the minimumvalue among the power spectrum estimates of all the segments.

Output Arguments

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More About

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Welch’s Overlapped Segment Averaging Spectral Estimation

The periodogram is not a consistent estimator of the true power spectral density of a wide-sense stationary process. Welch’s technique to reduce the variance of the periodogram breaks the time series into segments, usually overlapping.

Welch’s method computes a modified periodogram for each segment and then averages these estimates to produce the estimate of the power spectral density. Because the process is wide-sense stationary and Welch’s method uses PSD estimates of different segments of the time series, the modified periodograms represent approximately uncorrelated estimates of the true PSD and averaging reduces the variability.

The segments are typically multiplied by a window function, such as a Hamming window, so that Welch’s method amounts to averaging modified periodograms. Because the segments usually overlap, data values at the beginning and end of the segment tapered by the window in one segment, occur away from the ends of adjacent segments. This guards against the loss of information caused by windowing.

References

[1] Hayes, Monson H. Statistical Digital Signal Processing and Modeling. New York: John Wiley & Sons, 1996.

[2] Stoica, Petre, and Randolph Moses. Spectral Analysis of Signals. Upper Saddle River, NJ: Prentice Hall, 2005.

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced before R2006a

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The pwelch function supports single-precision variable-size window inputs for code generation.

The pwelch function supports single-precision inputs.

You can now use the Create Plot Live Editor task to visualize the output of pwelch interactively. You can select different chart types and set optional parameters. The task also automatically generates code that becomes part of your live script.

See Also

Apps

  • Signal Analyzer

Functions

  • periodogram | pmtm | pspectrum

Topics

  • Bias and Variability in the Periodogram
  • Spectral Analysis

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Welch’s power spectral density estimate (11)

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Welch’s power spectral density estimate (2024)

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