How to measure the peaks in my plot?
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I will keep the explanation, how my codes works, very short. I advise you to try this code yourself, so perhaps you understand it better that way. I have an audio-file and read it in my code. Now i switch from the time domain to the frequency domain by using the function FFT. But the only difference is, that i am performing an STFT on my audio signal. I do it every 30ms, until to the length of my signal. I am aware, that there are many different function in matlab, which also can perform this easily, but there are not giving me the results i need. Now, i am plotting many different frequency spectrums every 30ms. But i split up my signal in three frequency bands. They are called LOW, MEDIUM and HIGH. Basically, this means I have 3 different spectrums plotting every 30ms. The next step I do, is summing all the magnitudes from ONE frequency spectrum together, this means I have ONE VALUE per frequency spectrum, which are being squared.
Now, i have the power from every spectrum ! And all of these values are being plotted in my code. I am only plotting the power values, otherwise my code performance time would be extremely slow. Btw, the code looks long, but there are two for loop. In the first, i read the low spectrum and when it is finished, the second starts with the medium and high spectrum. Basically they are the same. I am aware, i can probably do that with findpeaks or something similar. But how can i do that?
clear;
clc;
%%MATLAB
%%read file
%_________________________________________
[y,fs]=audioread('Undertale - Megalovania.wav');
% audioread = read wav -file
% y = contains the audio signal
% fs = 44100
% 'UnchainMyHeart' = name of the wav-file
%_________________________________________
%%PARAMETER FOR STFT
%_________________________________________
t_seg=0.03; % length of segment in ms
fftlen = 4096; %FFT-Points
% Defining size of frequency bands
f_low= 1:200; %lower frequencies
f_medium= 201:600; %medium frequencies
f_high= 601:1000; %higher frequencies
%__________________________________________
%%CODE
segl =floor(t_seg*fs);
windowshift=segl/2;
% defining the size of the window shift
window=hann(segl);
% apply hann function on segment length (30 ms)
window=window.';
% transpose vector
si=1;
% defining start index
ei=segl;
% defining end index
N=floor( length(y)/windowshift - 1);
% Calculates the number, how often the window has to shift
% until to length of the audio signal
f1=figure;
% Generating new window
f=0:1:fftlen-1;
f=f/fftlen*fs;
% defining frequency vector
Ya=zeros(1,fftlen);
ValuesOfYc = NaN(1,N);
ValuesOfYd = NaN(1,N);
ValuesOfYe = NaN(1,N);
x =(1:N)*windowshift/fs;
% defining x-axis
for m= 1:1:N
y_a = y(si:ei);
% a segment is taken out from audio signal length(30ms)
y_a= y_a.*window;
% multiplying segment with window (hanning)
Ya=fft(y_a, fftlen);
% Applying fft on segment
Yb=abs(Ya(1:end/2)).^2;
% Squaring the magnitudes from one-sided spectrum
drawnow; % Updating the graphical values
figure(f1);
% Showing the power values
%%frequency bands
y_low = Yb(f_low); % LOW frequency spectrum
Yc=sum(y_low);
% Summing all the power values from one frequency spectrum together
% so you get one power value from one spectrum
ValuesOfYc(m) = Yc;
%Output values are being saved here, which are generated from the for
%loop
% m = start variable from for loop
subplot(2,1,1)
p=plot(x,ValuesOfYc,'r-');%,x, ValuesOfYd,'g-', x, ValuesOfYe,'b-' );
p(1).LineWidth =0.5;
xlabel('time (Audio length)')
ylabel('Power')
grid on
si=si+windowshift;
% Updating start index
ei=ei+windowshift;
% Updating end index
end
for o= 1:1:N
y_a = y(si:ei);
% a segment is taken out from audio signal length(30ms)
y_a= y_a.*window;
% multiplying segment with window (hanning)
Ya=fft(y_a, fftlen);
% Applying fft on segment
Yb=abs(Ya(1:end/2)).^2;
% Squaring the magnitudes from one-sided spectrum
drawnow; % Updating the graphical values
figure(f1);
% Showing the power values
%%frequency bands
y_medium = Yb(f_medium); % MEDIUM frequency spectrum
y_high = Yb(f_high); % HIGH frequency spectrum
Yd=sum(y_medium);
Ye=sum(y_high);
% Summing all the power values from one frequency spectrum together
% so you get one power value from one spectrum
ValuesOfYd(o) = Yd;
ValuesOfYe(o) = Ye;
%Output values are being saved here, which are generated from the for
%loop
% m = start variable from for loop
subplot(2,1,2)
p=plot(x, ValuesOfYd,'g-', x, ValuesOfYe,'b-' );
p(1).LineWidth =0.5;
xlabel('time (Audio length)')
ylabel('Power')
grid on
si=si+windowshift;
% Updating start index
ei=ei+windowshift;
% Updating end index
end
I hope you can read the PDF-file. In my plot(the red one), i want to measure the peaks (basically the coordinates) and the distance between them. How can I do that?
2 个评论
Rik
2017-2-21
Have you tried findpeaks yet? I just scrolled through the doc and I noticed you can even use the 'MinPeakProminence' switch to only get the larger peaks, so you get the true peaks. I would suggest setting that value to a percentage of the max-min.
采纳的回答
Rik
2017-2-21
[continued from comments]
With true peaks I meant the larger peaks, not just every local maximum. If you use the prominence you can filter them out easily, but you can use the strategy you propose.
[~,locs] = findpeaks(data)
real_peak_locs=intersect(find(data>5000),locs);
pks=data(real_peak_locs);
The variable pks will contain the value for all points that are a local maximum whose value is greater than 5000.
Although I still think this syntax is more elegant:
[pks,real_peak_locs] = findpeaks(data,'MinPeakProminence',0.75*(max(data)-min(data)));
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