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# meanfreq() - Signal Processing

### Syntax

### Example

### Output / Return Value

### Limitations

### Alternatives / See Also

### Reference

freq = meanfreq(x) estimates the mean normalized frequency, freq, of the power spectrum of a time-domain signal, x. examplefreq = meanfreq(x,fs) estimates the mean frequency in terms of the sample rate, fs. examplefreq = meanfreq(pxx,f) returns the mean frequency of a power spectral density (PSD) estimate, pxx. The frequencies, f, correspond to the estimates in pxx. freq = meanfreq(sxx,f,rbw) returns the mean frequency of a power spectrum estimate, sxx, with resolution bandwidth rbw. freq = meanfreq(___,freqrange) specifies the frequency interval over which to compute the mean frequency, using any of the input arguments from previous syntaxes. The default value for freqrange is the entire bandwidth of the input signal. example[freq,power] = meanfreq(___) also returns the band power, power, of the spectrum. If you specify freqrange, then power contains the band power within freqrange. meanfreq(___) with no output arguments plots the PSD or power spectrum and annotates the mean frequency.

freq = meanfreq(x)freq = meanfreq(x,fs) examplefreq = meanfreq(pxx,f) examplefreq = meanfreq(sxx,f,rbw)freq = meanfreq(___,freqrange)[freq,power] = meanfreq(___) examplemeanfreq(___)

Mean Frequency of ChirpsOpen This Example Generate 1024 samples of a chirp sampled at 1024 kHz. The chirp has an initial frequency of 50 kHz and reaches 100 kHz at the end of the sampling. Add white Gaussian noise such that the signal-to-noise ratio is 40 dB. Reset the random number generator for reproducible results. nSamp = 1024; Fs = 1024e3; SNR = 40; rng default t = (0:nSamp-1)'/Fs; x = chirp(t,50e3,nSamp/Fs,100e3); x = x+randn(size(x))*std(x)/db2mag(SNR); Estimate the mean frequency of the chirp. Plot the power spectral density (PSD) and annotate the mean frequency.meanfreq(x,Fs) ans = 7.5032e+04 Generate another chirp. Specify an initial frequency of 200 kHz, a final frequency of 300 kHz, and an amplitude that is twice that of the first signal. Add white Gaussian noise.x2 = 2*chirp(t,200e3,nSamp/Fs,300e3); x2 = x2+randn(size(x2))*std(x2)/db2mag(SNR); Concatenate the chirps to produce a two-channel signal. Estimate the mean frequency of each channel.y = meanfreq([x x2],Fs) y = 1.0e+05 * 0.7503 2.4999 Plot the PSDs of the two channels and annotate their mean frequencies.meanfreq([x x2],Fs); Add the two channels to form a new signal. Plot the PSD and annotate the mean frequency.meanfreq(x+x2,Fs) ans = 2.1496e+05 Mean Frequency of SinusoidsOpen This ExampleGenerate 1024 samples of a 100.123 kHz sinusoid sampled at 1024 kHz. Add white Gaussian noise such that the signal-to-noise ratio is 40 dB. Reset the random number generator for reproducible results.nSamp = 1024; Fs = 1024e3; SNR = 40; rng default t = (0:nSamp-1)'/Fs; x = sin(2*pi*t*100.123e3); x = x + randn(size(x))*std(x)/db2mag(SNR); Use the periodogram function to compute the power spectral density (PSD) of the signal. Specify a Kaiser window with the same length as the signal and a shape factor of 38. Estimate the mean frequency of the signal and annotate it on a plot of the PSD.[Pxx,f] = periodogram(x,kaiser(nSamp,38),[],Fs); meanfreq(Pxx,f); Generate another sinusoid, this one with a frequency of 257.321 kHz and an amplitude that is twice that of the first sinusoid. Add white noise.x2 = 2*sin(2*pi*t*257.321e3); x2 = x2 + randn(size(x2))*std(x2)/db2mag(SNR); Concatenate the sinusoids to produce a two-channel signal. Estimate the PSD of each channel and use the result to determine the mean frequency.[Pyy,f] = periodogram([x x2],kaiser(nSamp,38),[],Fs); y = meanfreq(Pyy,f) y = 1.0e+05 * 1.0013 2.5732 Annotate the mean frequencies of the two channels on a plot of the PSDs.meanfreq(Pyy,f); Add the two channels to form a new signal. Estimate the PSD and annotate the mean frequency.[Pzz,f] = periodogram(x+x2,kaiser(nSamp,38),[],Fs); meanfreq(Pzz,f); Mean Frequency of Bandlimited SignalsOpen This Example Generate a signal whose PSD resembles the frequency response of an 88th-order bandpass FIR filter with normalized cutoff frequencies rad/sample and rad/sample. d = fir1(88,[0.25 0.45]); Compute the mean frequency of the signal between rad/sample and rad/sample. Plot the PSD and annotate the mean frequency and measurement interval.meanfreq(d,[],[0.3 0.6]*pi); Output the mean frequency and the band power of the measurement interval. Specifying a sample rate of is equivalent to leaving the rate unset.[mnf,power] = meanfreq(d,2*pi,[0.3 0.6]*pi); fprintf('Mean = %.3f*pi, power = %.1f%% of total \n', ... mnf/pi,power/bandpower(d)*100) Mean = 0.373*pi, power = 75.6% of total Add a second channel with normalized cutoff frequencies rad/sample and rad/sample and an amplitude that is one-tenth that of the first channel.d = [d;fir1(88,[0.5 0.8])/10]'; Compute the mean frequency of the signal between rad/sample and rad/sample. Plot the PSD and annotate the mean frequency of each channel and the measurement interval.meanfreq(d,[],[0.3 0.9]*pi); Output the mean frequency of each channel. Divide by .mnf = meanfreq(d,[],[0.3 0.9]*pi)/pi mnf = 0.3730 0.6500