MEEN 260 MEEN260 lab8 – Texas A&M
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MEEN 260 MEEN260 lab8 – Texas A&M
SOUND MEASUREMENT,DATA ACQUISITION,AND FILTERING
Created June 20, 2007, B. Rasmussen Last updated Oct. 29, 2010, D. Freeman and R. Langari Purposes of the Experiment
1)To illustrate basic concepts in acoustic measurement
2)To demonstrate the limitations of data acquisition, including quantization and aliasing
3)To introduce frequency domain analysis of signals and the purpose of filters
Introduction
Whenever a sound is made, a pressure wave travels through a medium (such as air) and vibrates our eardrums. This same principle can be used to convert sound information into an electrical form so that the experimenters can visualize, interpret, and analyze the sound information.
Description
MEEN 260 MEEN260 lab8 – Texas A&M
SOUND MEASUREMENT,DATA ACQUISITION,AND FILTERING
Created June 20, 2007, B. Rasmussen Last updated Oct. 29, 2010, D. Freeman and R. Langari Purposes of the Experiment
1)To illustrate basic concepts in acoustic measurement
2)To demonstrate the limitations of data acquisition, including quantization and aliasing
3)To introduce frequency domain analysis of signals and the purpose of filters
Introduction
Whenever a sound is made, a pressure wave travels through a medium (such as air) and vibrates our eardrums. This same principle can be used to convert sound information into an electrical form so that the experimenters can visualize, interpret, and analyze the sound information.
In addition to visualizing the amplitude of a sound wave (electrically) over time, we can also look at the frequency content of the sound signal. Any signal, including a sound wave, can be thought of as the sum of different frequency sine waves (where each frequency wave has a specific amplitude and phase angle).
Simply put, we can use the Fast Fourier Transform (FFT) algorithm to look at how much of each frequency the sound signal contains. In addition to the frequencies we expect to see in a signal, some undesirable frequencies may also be present.
Filters allow us to select which frequencies we care about, and discard certain frequencies that are undesirable (such as noise). Various types of filters exist including low-pass, high-pass, band-pass, etc.
There are also many different ways of constructing each filter type with each filter implementation having its own specific characteristics. More information on the FFT and filters will be provided in the theory section. During this exercise, the experimenters will use a microphone element to convert a sound wave into an electrical signal.
This signal will be then digitized using a DAQ device. LabView will be used to visualize the time domain sound signal and compute its Fast Fourier Transform for viewing in the frequency domain.
Finally, a LabVIEW application will be constructed to filter undesired frequencies from the signal and play the clarified signal back.
Theory
Electret Microphone Elements One way to convert sound pressure waves into an electrical signal is using an electret microphone element. A picture of such an element is shown below: Figure 1: Electret Microphone Element
Inside the electret microphone element, a dielectric material is made to hold a permanent charge. When the element vibrates, the internal capacitance changes and an electrical signal is produced.
A variety of additional components inside the microphone element act as a small output amplifier.
Data Acquisition Limitations
A data acquisition system is used to measure, digitize, and record/display an electrical signal. In doing so, there are some limitations or potential problems of which every engineer should be aware.
The first is the phenomenon of quantization. When an analog signal is digitized using an
MEEN 260 MEEN260 lab8 – Texas A&M
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