ita_demo_intensivkurs_messtechnik.m 15.9 KB
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%% Intensivkurs Raumakustik Messtechnik (student version)

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% <ITA-Toolbox>
% This file is part of the application Laboratory for the ITA-Toolbox.
% All rights reserved.
% You can find the license for this m-file in the application folder.
% </ITA-Toolbox>
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%
% _2015 - JCK, GKB
% toolbox-dev@akustik.rwth-aachen.de
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%% SLIDES - TOOLBOX INTRODUCTION
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Initialization
% The first toolbox command you should know is 'ccx'. It clears all
% variables, closes all plots, basically brings MATLAB back to its initial
% state.
ccx;
 
% Select your sound card in the ita_preferences, tab 'IO Settings' as
% 'Recording' and 'Playing Device'
ita_preferences;
 
%% Introduction to class |itaValue|
% itaValue is a toolbox class to store values with a physical unit
 
m = itaValue(5.2,'kg');     % Mass [kg]
g = itaValue(9.81,'m/s^2'); % Acceleration [m/s^2]
 
% The units are consolidated upon multiplication or division
F = m*g %#ok<NOPTS> % Force [N]
 
%% Introduction to class |itaCoordinates|
% itaCoordinates is a toolbox class to store and transform coordinate data
 
% Create object with 20 coordinate points
N     = 20;
coord = itaCoordinates(N);
 
% The properties of the object can be accessed with the '.' operator.
% For instance the number of coordinate points
coord.nPoints
 
% As an example we assign random values to all x and y components
coord.x = rand(N,1);
coord.y = rand(N,1);
% To assign the same value to a component of all points we use a scalar
coord.z = 1;
 
% Examine the Cartesian coordinates
coord.cart      % all data
 
% Or their spherical or cylindrical transformations
coord.sph
coord.cyl
 
% Visualization
scatter(coord);          % Plot the points only
plot(coord);   % Plot the points, connected in order        

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ADVANCED %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% There are toolbox functions that create specific distributions of
% coordinate points for you. For instance a spherical equiangular sampling
% has points every 5 degrees in horizontal and azimuthal direction.
equi = ita_generateSampling_equiangular(5,5);

% For the latter it is more interesting to have a non-uniform function
% for instance a cardioid.
surf(equi, 1 + cos(equi.theta)); % Points connected to a closed surface
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%% Introduction to class |itaAudio|
% itaAudio is the toolbox class used to store audio data
 
sine = itaAudio; % Create empty object of class itaAudio
 
sine.comment            = 'Sine'; % Set comment for entire object
sine.trackLength        = 2; % Set length in seconds
 
% timeVector contains the list of sampling times
t = sine.timeVector;
 
% Let's generate the actual sine by hand!
f = 1000; % Set frequency
 
% The frequency and sampling times are the argument of a sine.
% Store the resulting amplitude values in the audio object.
sine.time   = sin(2* pi * f * t);
sine = sine * itaValue(1,'Pa'); % Assign a physical unit
 
sine2 = sine; % sine2 is a copy of sine.
sine2 = sine2 * 5; % Amplify through multiplication (here to 5 Pa)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ADVANCED %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
sine.channelNames{1}    = 'First channel'; % Comment for first channel
sine2.channelNames{1} = 'Copy'; % Set new channel name
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 

% Several audio objects can be merged to one object with multiple channels
sine_comp = merge(sine, sine2);
 
% Plot
sine_comp.plot_time;    % Time domain, linear amplitude scale
sine_comp.plot_time_dB; % Time domain, dB amplitude scale
% Use the keys A, * and / to look at all or singe channels and flip through
% them.
 
%% SLIDES - DIGITAL SIGNAL PROCESSING
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%% Generate a sine, the easy way
ccx; % Clear workspace
 
f           = 1000;     % Sine frequency in Hz.
amplitude   = 1;        % Amplitude.
fftDegree   = 14;       % Signal length, see explanation below.
sr          = 44100;    % Sampling rate in 1/s.
 
% fftDegree: For the efficient fast Fourier transform (FFT) a signal with a
%            number of samples in the power of two is required. Thus the
%            signal lengths are varied in steps.
%            Example: 
%                       - Sampling rate: 44100 1/s
%
%                       - Number of samples:  2^17 = 131072
%                       - Resulting signal length: 2^17/44100Hz = 2.9722 s
%                       
%                       - Number of samples: 2^18 = 262144
%                       - Resulting signal length: 2^18/44100 = 5.9443 s
 
% Use the function ita_generate to generate the sine
% HINT: You'll find examples for each function in the help and the actual
% function file. Try "help ita_generate" and "edit ita_generate".
sine = ita_generate('sine',amplitude,f,sr,fftDegree);
 
% We limit the number of samples in the time domain for later
% considerations
sine.nSamples = 890;
 
%% Discrete Fourier Transform
 
% Look at the signal in the time domain
% HINT: ".pt" is short for ".plot_time"
sine.pt
% Now have a look at the DFT of the signal. This representation shows you 
% which frequencies are included in the signal.
% HINT: ".pf" is short for ".plot_freq"
sine.pf
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TASK %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Look at the sine in the time domain. You will see that it ends suddenly
% before it reaches a 0 amplitude level. This is a jump in the signal.
% If you look at the sine in the frequency domain you will see that it
% has a dominant 1000 Hz component as intended. However, the other
% frequency components are not completely gone. That's the case due to the 
% jump in the signal. Only if the signal consists of a full sine period
% will we see just the expected 1000Hz component in the frequency domain.
% Find the right number of samples to fit 20 full periods should into given 
% time slot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% YOUR CODE HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 1 period of 1kHz sine = 1/1kHz = 0.001
% 20 full periods with 441001/s sampling rate => 20 * 0.001 * 44100 = 882
sine.nSamples = 882;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
% Check the resulting sine
sine.pt
sine.pf

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DEMO TASK %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Generate a white noise signal and look at both domains
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% YOUR CODE HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
noise = ita_generate('noise',1,44100,15);
noise.pt
noise.pf
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
%% Load demo sound for further demos
sound = ita_demosound; % Built-in toolbox demo sound
 
%% Quantization
 
% Re-quantize the signal and listen to the effect
quant_8bit = ita_quantize(sound,'bits',8);
quant_8bit.play;
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DEMO TASK %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Quantize the signal with different bit values. how is the signal
% changing?
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% YOUR CODE HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
quant_4bit = ita_quantize(sound,'bits',4);
quant_4bit.play;
quant_4bit.pt

quant_2bit = ita_quantize(sound,'bits',2);
quant_2bit.play;
quant_2bit.pt
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
%% Sampling and filtering
 
% We can simulate aliasing by artificially reducing the sampling rate.
% In this example, the sampling rate is reduced by a factor of 20, meaning
% that only every 20th sample would have been recorded.
% The new sampling rate will be 2205 1/s.
% The highest representable (Nyquist) frequency is 1102.5 Hz
 
sound1 = sound;
% Take only every 20th sample -> 20 times lower sampling rate.
sound1.timeData = sound1.timeData(1:20:end);
 
% Adjust the sampling rate information in the objects meta data
sound1.samplingRate = sound1.samplingRate / 20;
 
% Play sound
sound1.play;
 
% Plot
sound1.pf;
 
% Reducing the sampling rate like this results in aliasing. The energy
% contained in frequencies higher than the maximum frequency
% (here: 1102.5 Hz) will be assigned to lower frequencies.
 
sound2 = sound;
% To avoid aliasing we have to make sure, that no
% information above our Nyquist frequency exists.
sound2_filtered = ita_filter_bandpass(sound2,'upper',1000);
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DEMO TASK %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Reduce the sampling rate of the filtered signal.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% YOUR CODE HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
sound2.timeData = sound2_filtered.timeData(1:20:end);
sound2.samplingRate = sound2.samplingRate / 20;
sound2.play;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
% Compare the spectra of sound, sound1 and sound2. Do you see aliasing 
% effects in the first one?
plot_sound = merge(sound, sound1, sound2);
plot_sound.plot_freq;
 
% The representation of the frequencies contained within a signal over the
% time is called spectrogram. Have a look at it for the demo sound!
sound.plot_spectrogram
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DEMO TASK %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Simulate the transmission of the music signal over the telephone.
% Telephones only transmit frequencies between 300 Hz and 4000 Hz.
% Use ita_filter_bandpass to filter the signal and cut information
% that is not transmitted
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% YOUR CODE HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
f_low  = 300;
f_high = 4000;
sound_telefon = ita_filter_bandpass(sound,'lower',f_low,'upper',f_high);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
% Have a look at the spectrogram of the filtered signal.
% What are the differences?
sound_telefon.plot_spectrogram
 
%% Influence of the phase;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ADVANCED %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Take the music signal and delete the phase information. 
 
g_nophase = sound;
% Get rid of the phase information.
g_nophase.freqData = abs(sound.freqData) .* exp(1i*2*pi*zeros(sound.nBins,1));
g_nophase.plot_freq_phase
g_nophase.play
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DEMO TASK %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Add a random phase but take the original magnitude of the spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%% YOUR CODE HERE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
g_randphase = sound;
g_randphase.freqData = abs(sound.freqData) .* exp(1i*2*pi*rand(sound.nBins,1));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    
g_randphase.play;
g_randphase.plot_freq_phase;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
%% SLIDES - LTI SYSTEMS
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%% Generate artificial room impulse response                                
% In this section a simulated room impulse response (RIR) will be
% generated basing on a noise signal.
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% Generate RIR
fftDegree   = 17; % length of IR is 2^fftDegree samples
L           = [8 5 3]/10; % room geometry (x,y,z) in meters
fmax        = 10000; % highest eigenfreuency in Hertz
r_source    = [5 3 1.2]/10; % positions
T           = 1; %reverberation time of each eigenmode -> definition of modal damping
RIR         = ita_roomacoustics_analytic_FRF_book(itaCoordinates(L), itaCoordinates(r_source), itaCoordinates([0 0 0]),'f_max',fmax,'T',T,'fftDegree',fftDegree,'pressuremode',false);
RIR.channelNames{1} = 'ideal RIR';
win_vec     = [1.5 2]; % time window parameters in seconds
RIR_cut     = ita_time_window(RIR,win_vec,'time','crop');
RIR_cut     = ita_normalize_dat(RIR_cut); % to obtain a scale just below 0dB in the end only for demonstration purposes
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%% Auralize                                                                
s = ita_demosound;
g = ita_convolve(s,RIR_cut); % apply the impulse response to music signal
g.play
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%% Measurement Dummy Class for virtual/emulated measurement                
MS = itaMSTFdummy; % get measurement setup object

% specify measurement parameters
MS.fftDegree             = 19;  % Set length of sweep signal
MS.stopMargin            = 1;   % Time to wait until the system decays
MS.freqRange             = [20 20000]; % Frequency range for the measurement
MS.outputamplification   = -30; % level below maximum amplitude in dBFS (full scale)
MS.averages              = 1;   % number of measurements to be averaged to get a mean result
MS.samplingRate          = RIR_cut.samplingRate;  % sampling Rate of the system

% specify the device under test (DUT) to be measured
MS.nonlinearCoefficients = 1; % only linear transmission or e.g. [1 0.1 0.1] for g = 1*s^1 + 0.1*s^2 + 0.1*s^3
MS.noiselevel            = -30; % noise level below maximum amplitude in dBFS
MS.systemresponse        = RIR_cut; % impulse response of the system
MS.nBits                 = 24; % Quantization of the system

% simulation of the actual measurement
h_measured               = MS.run; % get the measured impulse response with nonlinearities, quantization and noise
h_measured.plot_time_dB % plot IR in dB

%% How to obtain a higher SNR ?                                            
% Usually the noise level during room acoustical measurements is quite
% high. To improve the signal to noise ratio (SNR), we can measure the IR
% several times and average the results. This improves the SNR since noise 
% is not correlated (+3dB), but the measurement signal is correlated
% (+6dB).

% Close all plots
close all

% Set number of averages, feel free to experiment!
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MS.averages = 4;
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% % Average! - this is what happens below, when you run your measurement
% for idx = 1:averages
%     awgn   = 10^(-SNR / 20) * ita_generate('noise', 1, sr, s.nSamples); % Generate new additive measurement noise for every measurement!
%     g(idx) = s*RIR + awgn;  % Add background noise
% end
% g = sum(g)/averages;

% Measurement with deconvolution
h_measured_av          = MS.run ;
h_measured_av.channelNames{1} = ['with ' num2str(MS.averages) ' averages'];

% Merge
RIRplot = RIR_cut;
RIRplot.nSamples = h_measured.nSamples;% zero padding to obtain the same length
res     = merge(h_measured, h_measured_av,RIRplot);

% Plot and compare!
res.plot_time_dB

%% Non-linearities - Spectrogram of g(t)                                    
MS2 = itaMSTFdummy;
MS2.outputamplification   = 0; % play around with different values: -40, -20
MS2.nonlinearCoefficients = [1 0.1 0.1 0.1];
MS2.noiselevel = []; % no noise, to just see the non-linearities
MS2.systemresponse = []; % no noise, to just see the non-linearities
MS2.nBits = 24; % Lots of values...
g   = MS2.run_raw; % get system output without deconvolution
g.plot_spectrogram

%% Impulse response of non-linear system                                   
h = MS2.run; % incl. deconvolution
h.plot_time_dB 

%% Nonlinearities, RIR, noise and quantization                             
MS.outputamplification   =  0;
MS.nonlinearCoefficients = [1 0.1 0.1 0.1]; % set nonlinear coefficients
MS.noiselevel            = -0; % very high noise level
MS.systemresponse        = RIR_cut;
MS.nBits                 = 16;
h                        = MS.run;
h.plot_time_dB
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