# Finding the right measure to compare sound signals in the frequency domain

I am comparing the spectral contents of some sound signals with MATLAB. I have quite a few measures but wanted one that represents the centre of gravity of the spectrum.

I used SpecCentroid() found in File Exchange to calculate the linear mean spectrum centroid. Then I also used the function meanfreq() and medianfreq() from MATLAB.

I am struggling to understand (mathematically and perceptually) the differences of these measures and so I am not too sure which one to choose to best compare signals between each other.

Thanks a lot.

EDIT:

I have 2 types of sounds from different sources and try to compare their spectral 'signatures' to predict if they both would be relevant to some animal species. At this point, I am only interested in the frequency domain (although ultimately the time domain will be as equally important). I have so far compared the peak frequencies. However, as the signals are quite noisy, I was wondering if other measures could be more appropriate. That is why I was exploring the centroid, mean, median options.

Here is the same plot as before in the linear scale: EDIT 2:

After Laurent Duval's answer and suggestions, I tested the estimated peak location, with different weights. For the signal example given above, I got:

\begin{array}{|c|c|c|} \hline\text{$p$} & \text{$f$} & \text{dispersion $d$} \\ \hline 1 & 979.6 & 979 \\ 2 & 493.12 & 627\\ 3 & 327 & 295\\ 4 & 296 & 135\\ 5 & 291.14 & 65\\ 6 & 285 & 35\\\hline \end{array}

The peak frequency as found with findpeaks() was $285$.

Let's take one of my more noisy signals:

\begin{array}{|c|c|c|} \hline\text{$p$} & \text{$f$} & \text{dispersion $d$} \\ \hline 1 & 1460 & 826\\ 2 & 1295 & 768\\ 3 & 1122 & 719\\ 4 & 955 & 648\\ 5 & 811 & 562\\ 6 & 698 & 470\\\hline \end{array}

The peak frequency as found with findpeaks() was $427$. Example at $p=4$: It is quite clear that the dispersion is much higher than for the last (less noisy) signal.

• Mean, median, centroid or maximum are location estimators that are especially meaningful on unimodal distributions. Your spectrum is multimodal, and somewhat flat due to the (possible) log/dB scale. Could you please retry on the power spectrum for instance? And detail why you want to compare sounds? Are they stationnary? Dec 4 '15 at 9:25
• Thanks for trying to help me! I added some details in my questions on what I am trying to achieve. The sounds are not stationary by definition, however I treat them as such (the sound are created by a hit, and the response is treated as a stationary process, if that makes any sense?). Not sure I got what you meant: 'Could you please retry on the power spectrum for instance?' I believe that the plot I attached is actually the power spectrum? I will edit my post and attach the same plot in the linear scale if that helps? Dec 5 '15 at 10:25
• I see. Proposal in a few minutes Dec 5 '15 at 11:37

[EDIT: a related discussion on (power) spectrum centroids can be found in How to calculate the mean/center frequency of the spectrum?] Let us assume you have a relatively concentrated peak, with noise. I assume your maximum frequency is ok. If too high, you can trim the spectrum to lower frequency, which could help.

The code below gives a simple model, estimates a peak location, and a gross measure of dispersion (or uncertainty). It is based on a weighted average or center of mass. If the peak is narrow, the center of mass (and the median) will be drifted away toward the center of the frequency axis ($f_i$'s). Because even if low, the noise spreads. This is an illustration of Archimedes' lever principle:

Give me a place to stand on, and I can move the earth. To achieve your goal, you can give more weight to your spectrum of amplitude $a_i$, and compute an estimated peak location $\bar{f}$: $$\bar{f} = \frac{\sum_i w_i f_i}{\sum_i w_i}\,.$$

If you choose $w_i = a_i$, you get the standard average. This is not enough. You can increase the peak weight with a power: $w_i = a_i^p$. This is the aim of weightPower = 4 in the code. The location is given by the red o, and the x are the left and right bounds derived from a weighted standard deviation. The higher the $p$, the sharper the estimation, with a risk of major errors if the data differs from the model.

Please report your tests, to adjust the answer. And the result follows. With $p=1$, you get $360$, with $p=2$, you get $243$, with $p=4$, $\bar{f}=201$, quite close to $200$ in my model. If you need much higher robustness, you can go for weighed medians instead of weighed means. freqAxis = linspace(0,1000,1000)';
freqPeak = 200;
freqPeakAmplitude = 1;
freqGGDExponent = 2;
freqGGDWidth = 50;
freqNoise = 0.2;

dataSpectrum = exp(-((freqAxis-freqPeak)/freqGGDWidth).^freqGGDExponent)+freqNoise*rand(size(freqAxis));

weightPower = 4;
freqEstLocation = sum((dataSpectrum.^weightPower).*freqAxis)/sum(dataSpectrum.^weightPower);
freqEstDispersion = sqrt(sum((dataSpectrum.^weightPower).*(freqAxis-freqEstLocation).^2)/sum(dataSpectrum.^weightPower));

freqEstAmplitude = interp1(freqAxis,dataSpectrum,freqEstLocation);
clf;hold on
plot(freqAxis,dataSpectrum);
plot(freqEstLocation,freqEstAmplitude,'or');
plot([freqEstLocation-freqEstDispersion freqEstLocation+freqEstDispersion],freqEstAmplitude,'xr');
axis tight;grid on

• Great! That makes sense. I will test this and report my answers in a new edit. (ps love the picture too!) Dec 6 '15 at 4:07
• Thank you very much Laurent! It is working well. What should be my arguments to choose the weight power (p)? I am guessing I need it to be permanent throughout the analysis so how should I choose it? Also, what is the problem with using the spectral centroid in this case? Dec 6 '15 at 4:46
• Estimating amounts to choosing. The proposed model is unimodal. Parameter $p$ could be database dependent, but signal independent. I usually choose among ${1,2,3,4}$, as those powers relate to mean, centroïd, skewness and kurtosis. Centroid relates to $p=2$, the lever was not strong enough. Your last spectra is very multimodal, there are caveat. You may need to classify with more than one single feature. Or include the energy of the harmonics, an average slope of the spectrum, a decay of the maxima... Dec 6 '15 at 10:52