Getting Started With Analog Input (Data Acquisition Toolbox)

Data Acquisition Toolbox

Acquiring Data with a Sound Card

Suppose you must verify that the fundamental (lowest) frequency of a tuning fork is 440 Hz. To perform this task, you will use a microphone and a sound card to collect sound level data. You will then perform an FFT on the acquired data to find the frequency components of the tuning fork. The setup for this task is shown below.

Configuring the Data Acquisition Session

As described in "The Data Acquisition Session", using the Data Acquisition Toolbox to acquire data involves these steps:

Create an analog input object
Add hardware channels to the device object and assign values to properties
Execute the device object
Retrieve acquired data from the data acquisition engine For this application, you will acquire one second of sound level data on one sound card channel. Since the tuning fork vibrates at a nominal frequency of 440 Hz, the sound card sampling rate can be set to its lowest sampling rate of 8000 Hz. Even at this lowest rate, we are guaranteed not to experience any aliasing effects since 8000 Hz is well above the expected Nyquist frequency. After you have set the tuning fork vibrating and placed it near the microphone, you will trigger the acquisition one time using a manual trigger. The complete data acquisition session for the sound card is shown below.

Initialization: Create the analog input object AI for a sound card. The installed adaptors and hardware ID's are found with daqhwinfo.

AI = analoginput('winsound'); %AI = analoginput('nidaq',1);

Configuration: Add one channel to AI, assign values to the basic setup properties, and create the variables blocksize and Fs, which are used for subsequent analysis. The actual sampling rate is retrieved since it may be set to a value that differs from the specified value.

chans = addchannel(AI, 1); %chans = addchannel(AI, 0);
duration = 1; %1 second acquisition
set(AI, 'SampleRate', 8000);
ActualRate = get(AI, 'SampleRate');
set(AI, 'SamplesPerTrigger', duration*ActualRate);
set(AI, 'TriggerType', 'Manual');
blocksize = get(AI,'SamplesPerTrigger');
Fs = ActualRate;

Execution: Start AI, issue a manual trigger, and extract all data from the engine. Before start is issued, you should begin inputting data from the tuning fork into the sound card (whistling will work as well).

start(AI);
trigger(AI)
data = getdata(AI);

Termination: Delete AI.

delete(AI)

You should be aware that only one application can access the sound card at any time. For example, if you are using the Windows Sound Recorder and issue the start command as shown above, the toolbox returns this error.

??? Error using ==> daqdevice/start
Device 'Winsound' already in use.

You must close the Sound Recorder application and reissue start.

MATLAB Analysis

For this experiment, analysis consists of finding the frequency components of the tuning fork and plotting the results. To do so, the function daqdocfft was created. This function calculates the FFT of data, and requires the values of SampleRate and SamplesPerTrigger as well as data as inputs.

[f, mag] = daqdocfft(data, Fs, blocksize);

daqdocfft outputs the frequency and magnitude of data, which can then be plotted. daqdocfft is shown below.

function [f, mag] = daqdocfft(data,Fs,blocksize) 
%DAQDOCFFT calculates the FFT of X using sampling frequency FS 
%and the SamplesPerTrigger provided in BLOCKSIZE.
xFFT = fft(data);
xfft = abs(xFFT);
ind = find(xfft==0); % Avoid taking the log of 0;
xfft(ind) = 1e-17;
mag = 20*log10(xfft);
mag = mag(1:blocksize/2);
f = (0:length(mag)-1)*Fs/blocksize;
f = f(:);

The results are plotted below.

plot(f, mag)
grid on 
ylabel('Magnitude (dB)')
xlabel('Frequency (Hz)')
title(sprintf('daqdoc3\\_1\nFrequency Components of Tuning 
Fork'))

This plot shows the fundamental frequency around 440 Hz and the first overtone around 880 Hz. A simple way to find actual fundamental frequency is

ymax = max(mag);
find(mag==ymax)

The answer is 441 Hz.

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