PERFORMING

DFT ON THE

INPUT SIGNAL

USING NI-USB6008

PROJECT

REPORT

BY:GEETANJALI PRASAD REG. NO.- 110906586 & VERNIKA BHATIA REG.NO.-110906772

PERFORMING DFT ON THE INPUT SIGNAL USING

NI-USB6008

PREREQUISITES-

1. LABVIEW 2010

2. MICROPHONE HARDWARE

3. NI-USB6008

AIM

to build a microphone circuit and acquire mirophone voltage data using ni-usb6008 and

hence performing a dft and observing variation using labview.

CIRCUIT SPECIFICATIONS- COMPONENTS-

1. CUI-CMB 6544PF MICROPHONE

2. 1 MICROFARAD CAPACITOR

3. I KILO-OHM RESISTOR

4. NI-USB6008

5. CONNECTING WIRES

the microphone has 2 terminals on the backside. the terminal 2 is wired with the case of the

microphone. terminal 2 from the tabs is connected to ground. terminal 1 is connected to 1k

ohm resistor which is connected to +5v and 1 microfarad capacitor is connected to wire from

which voltage is to be measured. ( we choose ai-6 to measure the voltage from thecapacitor

connected wire)

CONNECTEDIRCUIT DIAGRAM-

NI-USB 6008 CONNECTION-

VI DEVELOPMENT

1.take a blank vi. to acquire voltage from ni-usb6008, go to express- go to input and use the

daq assistant.

2. configure the daq by acquiring signal under analog. select voltage. choose pin ai-6 and

change the samplingfrequency to 8000hz and samples to read to 800

3. to get converter from dynamic data to something useful we right click on data and go to

signal manipulation palette and choose dynamic data

4. connect the data o/p to dynamic data input where the resulting data type is a single

waveform that gives voltage and time data corresponding to it.

5. add the waveform graph on frontpannel and connect o/p of dynamic data to graph it.

6. run it. the observed dc offset is at 1.4v

6. on adding noise from voice we can observe some fluctuations int graph.

7. to find the frequency components an fft is to be performed.

8. go to signal processing->>waveform measure and >> fft power spectrum.

9. wire the dynamic o/p to power spectrum input and to know about waveform

components>> right click on power spectrum>> go to waveform components

10. go to o/p of power spectrum>> go to cluster , class and variant palette>>unbundle by

name and connect it to power spectrum o/p.

11. create 2 indicators for frequency and change in frequency. create an array for

magnitude response.

12. connect a waveform graph to power spectrum output to look at fft variation.

13. on connecting the freq domain waveform graph and running the system we get an fft

on

the graph-

we get a max. dc offset with the value 1.75.

14. on inputting a human voice as input signal from microphone we get an fft on

the graph as-

15. For the above graph to find the maximum value of the array function of magnitude, we

go to ARRAY>>min-max function. Connect input of min-max function to magnitude

indicator wire.

16. We connect an indicator to max. index o/p of min-max array which gives the array

value at which max . frequency component occurs .

17. As we have a df value of 10hz, the first value in array corresponds to a value of 10 hz,

the 2nd one to 20hz and so on.

18. To find the max. value from the array we can make the first two values of the array 0

by connecting a replace array subset .

19. Output array values are displayed as follows

The first two array values are 0 as are indicated by replace array subset.

20. Min max value of the output array is found.

21. Max.index * df shows the max. value excluding the dc offset that is occurring in the

signal

OUTPUT VALUE IS :-

CONNECTIONS

CONNECTION OF TERMINAL2 OF MICROPHONE TO GROUND AND CAPACITOR TO AI6.

RESISTOR CONNECTED WIRES GO TO +5V.

OVERALL CONNECTION WITH THE MICROPHONE-

Technical Analysis of the project

The Fast Fourier Transform (FFT) and the power spectrum are powerful tools for analyzing and

measuring signals from plug-in data acquisition (DAQ) devices. For example, you can

effectively acquire time-domain signals, measure the frequency content, and convert the

results to real-world units and displays as shown on traditional benchtop spectrum and

network analyzers. By using plug-in DAQ devices, you can build a lower cost measurement

system and avoid the communication overhead of working with a stand-alone instrument.

Plus, you have the flexibility of configuring your measurement processing to meet your needs.

To perform FFT-based measurement, however, you must understand the fundamental issues

and computations involved. This application note serves the following purposes

. Describes some of the basic signal analysis computations,

Discusses antialiasing and acquisition front ends for FFT-based signal analysis,

Explains how to use windows correctly,

Explains some computations performed on the spectrum, and

Shows you how to use FFT-based functions for network measurement.

The basic functions for FFT-based signal analysis are the FFT, the Power Spectrum, and the

Cross Power Spectrum.

Using these functions as building blocks, you can create additional measurement functions

such as frequency response,

impulse response, coherence, amplitude spectrum, and phase spectrum.

FFTs and the Power Spectrum are useful for measuring the frequency content of stationary or

transient signals. FFTs produce the average frequency content of a signal over the entire time

that the signal was acquired. For this reason, you should use FFTs for stationary signal analysis

or in cases where you need only the average energy at each frequency line. To measure

frequency information that is changing over time, use joint time-frequency functions such as

the Gabor Spectrogram.This application note also describes other issues critical to FFT-based

measurement, such as the characteristics of the signal acquisition front end, the necessity of

using windows, the effect of using windows on the measurement, and measuring noise versus

discrete frequency components.

Future use of our project

We can interface the our labview g code with with niusb6008 and inspite of givng only one

input voice signal ,we can give two input voice signal,then we can perform fft and extract the

frequency components from the components two signals . when we do this we come to know

that each and evry voice signal hav different frequency component at particular instance of

time.

So this technique can be used to differentiate between two input voice signals.

We can use this for security purpose also, if we have a system which can be opened by only

one voice signal then we can use this technique because it will analyze the input voice and do

the required operations on the input signal and then compare with the existing or default

voice signal (the security code) and if it doesnt match then the system will not open else it will

open.