final version

Main.m

@@ -4,12 +4,55 @@
 #
 #Last modified: 02/03/2023 08:28:32
 #
-#
-#
-#
 #################
 
 pkg load signal;
 
+clc;
+close all;
+clear all;
+
+
+signal = csvread ('unknownsignal.csv');
+
+figure;
+plot(signal);
+title("raw Signal");
+xlabel("time");
+ylabel("Amplitude");
+
+% set signal of interest
+SStart = 100;
+SEnd = 400;
+
+Fs = 300;
+
+lenDomain = 1 + (SEnd - SStart);
+
+windowed_signal= zeros(lenDomain);
+
+% using blackman , we get the signal of interest
+windowed_signal = signal(SStart:SEnd) .* blackman(lenDomain)';
+
+figure;
+plot(SStart: SEnd, windowed_signal);
+title("Blackman signal windowing");
+xlabel("samples");
+ylabel("Amplitude");
+
+frequencySpectrum(windowed_signal, Fs, 0);
+
+%spectrogram(windowed_signal, Fs, 1/Fs, 1000*length(signal)/Fs);
+
+% filter using filter and butter
+
+[val, ind] = max(windowed_signal);
+
+figure;
+[b, a] = butter(6, 10/Fs);
+s = filter(b, a, 10*log10(windowed_signal/windowed_signal(ind)));
+plot(50:300, s(50:300));
+
+
+audiowrite("sound.wav", signal, Fs);
 
- x = csvread (filename)

frequencySpectrum.m

@@ -0,0 +1,48 @@
+function power = frequencySpectrum(signal, fs)
+%%%%%%%%%%%%%%%%%%
+%function frequencySpectrum(signal, fs)
+%
+% Task: Display the power spectrum of a given signal
+%
+% Input:
+%	- signal: the input signal to process
+%	- fs: the sampling rate
+%
+% Output: 
+%	- power: power spectrum of the signal
+%	
+%
+% Guillaume Gibert, guillaume.gibert@ecam.fr
+% 25/04/2022
+%%%%%%%%%%%%%%%%%%
+
+n = length(signal);          % number of samples
+
+y = fft(signal, n);% compute DFT of input signal
+power = abs(y).^2/n;    % power of the DFT
+
+[val, ind] = max(power); % find the mx value of DFT and its index
+
+% plots
+figure;
+
+subplot(1,3,1) % time plot 
+t=0:1/fs:(n-1)/fs; % time range
+plot(t, signal)
+xticks(0:0.1*fs:n*fs);
+xticklabels(0:0.1:n/fs);
+xlabel('Time (s)');
+ylabel('Amplitude (a.u.)');
+
+subplot(1,3,2) % linear frequency plot
+f = (0:n-1)*(fs/n);     % frequency range
+plot(f,power, 'b*'); hold on;
+plot(f,power, 'r');
+xlabel('Frequency (Hz)')
+ylabel('Power (a.u.)')
+
+subplot(1,3,3) % log frequency plot
+plot(f,10*log10(power/power(ind)));
+xlabel('Frequency (Hz)')
+ylabel('Power (dB)')
+

spectrogram.m

@@ -0,0 +1,38 @@
+function spectrogram(signal, samplingFreq, step_size, window_size)
+%%%%%%%%%%%%%%%%%%%%%%%
+%function spectrogram(signal, samplingFreq, step_size, window_size)
+% ex.:  spectrogram(signal, samplingFreq, step_size, window_size)
+%
+% Task: Plot the spectrogram of a given signal
+%
+% Inputs:
+%	-signal: temporal signal to analyse 
+%	-samplingFreq: sampling frequency of the temporal signal
+% 	-step_size: how often the power spectrum will be computed in ms
+%	-window_size: size of the analysing window in ms
+%
+% Ouput: None
+%
+% author: Guillaume Gibert (guillaume.gibert@ecam.fr)
+% date: 14/03/2023
+%%%%%%%%%%%%%%%%%%%%%%%
+
+figure;
+	subplot(2,1,1);
+t=0:1/samplingFreq:length(signal)/samplingFreq-1/samplingFreq;
+plot(t, signal');
+xlim([0 length(signal)/samplingFreq-1/samplingFreq]);
+ylabel('amplitude (norm. unit)');
+	subplot(2,1,2);
+step = fix(step_size*samplingFreq/1000);     % one spectral slice every step_size ms
+window = fix(window_size*samplingFreq/1000);  % window_size ms data window
+fftn = 2^nextpow2(window); % next highest power of 2
+[S, f, t] = specgram(signal, fftn, samplingFreq, window, window-step);
+S = abs(S(2:fftn*4000/samplingFreq,:)); % magnitude in range 0<f<=4000 Hz.
+S = S/max(S(:));           % normalize magnitude so that max is 0 dB.
+S = max(S, 10^(-40/10));   % clip below -40 dB.
+S = min(S, 10^(-3/10));    % clip above -3 dB.
+imagesc (t, f, log(S));    % display in log scale
+set (gca, "ydir", "normal"); % put the 'y' direction in the correct direction
+xlabel('time (s)');
+ylabel('frequency (Hz)');