all moodle files
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function [power, duration] = frequencySpectrum(signal, fs, pad)
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%%%%%%%%%%%%%%%%%%
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%function power = frequencySpectrum(signal, fs, pad)
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%
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% Task: Display the power spectrum (lin and log scale) of a given signal
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%
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% Input:
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% - signal: the input signal to process
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% - fs: the sampling rate
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% -pad: boolean if true, signal is padded with 0 to the next power of 2 -> FFT instead of DFT
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%
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% Output:
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% - power: the power spectrum
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%
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%
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% Guillaume Gibert, guillaume.gibert@ecam.fr
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% 25/04/2022
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%%%%%%%%%%%%%%%%%%
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n = length(signal); % number of samples
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if (pad)
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n = 2^nextpow2(n);
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end
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tic
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y = fft(signal, n);% compute DFT of input signal
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duration = toc;
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power = abs(y).^2/n; % power of the DFT
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[val, ind] = max(power); % find the mx value of DFT and its index
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% plots
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figure;
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subplot(1,3,1) % time plot
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t=0:1/fs:(n-1)/fs; % time range
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%pad signal with zeros
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if (pad)
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signal = [ signal; zeros( n-length(signal), 1)];
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end
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plot(t, signal)
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xticks(0:0.1*fs:n*fs);
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xticklabels(0:0.1:n/fs);
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xlabel('Time (s)');
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ylabel('Amplitude (a.u.)');
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subplot(1,3,2) % linear frequency plot
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f = (0:n-1)*(fs/n); % frequency range
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plot(f,power, 'b*'); hold on;
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plot(f,power, 'r');
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xlabel('Frequency (Hz)')
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ylabel('Power (a.u.)')
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subplot(1,3,3) % log frequency plot
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plot(f,10*log10(power/power(ind)));
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xlabel('Frequency (Hz)')
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ylabel('Power (dB)')
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function spectrogram(signal, samplingFreq, step_size, window_size)
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%%%%%%%%%%%%%%%%%%%%%%%
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%function spectrogram(signal, samplingFreq, step_size, window_size)
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% ex.: spectrogram(signal, samplingFreq, step_size, window_size)
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%
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% Task: Plot the spectrogram of a given signal
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%
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% Inputs:
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% -signal: temporal signal to analyse
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% -samplingFreq: sampling frequency of the temporal signal
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% -step_size: how often the power spectrum will be computed in ms
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% -window_size: size of the analysing window in ms
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%
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% Ouput: None
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%
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% author: Guillaume Gibert (guillaume.gibert@ecam.fr)
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% date: 14/03/2023
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%%%%%%%%%%%%%%%%%%%%%%%
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figure;
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subplot(2,1,1);
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t=0:1/samplingFreq:length(signal)/samplingFreq-1/samplingFreq;
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plot(t, signal');
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xlim([0 length(signal)/samplingFreq-1/samplingFreq]);
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ylabel('amplitude (norm. unit)');
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subplot(2,1,2);
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step = fix(step_size*samplingFreq/1000); % one spectral slice every step_size ms
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window = fix(window_size*samplingFreq/1000); % window_size ms data window
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fftn = 2^nextpow2(window); % next highest power of 2
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[S, f, t] = specgram(signal, fftn, samplingFreq, window, window-step);
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S = abs(S(2:fftn*4000/samplingFreq,:)); % magnitude in range 0<f<=4000 Hz.
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S = S/max(S(:)); % normalize magnitude so that max is 0 dB.
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S = max(S, 10^(-40/10)); % clip below -40 dB.
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S = min(S, 10^(-3/10)); % clip above -3 dB.
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imagesc (t, f, log(S)); % display in log scale
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set (gca, "ydir", "normal"); % put the 'y' direction in the correct direction
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xlabel('time (s)');
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ylabel('frequency (Hz)');
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