How Can I Measure Relative Amplitudes of Specific Frequencies in a Noise Field?

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SUMMARY

This discussion focuses on measuring the relative amplitudes of specific frequencies within a noise field using capacitive microphones attached to resonant tubes. Bert Rackett suggests utilizing a fast Fourier transform (FFT) to analyze the signal, recommending MatLab for plotting the Power Spectrum. The conversation highlights the importance of sampling at more than twice the highest frequency to ensure accurate detection and notes the potential impact of microphone positioning on the power spectrum due to destructive interference from surrounding walls.

PREREQUISITES
  • Understanding of capacitive microphones and their applications
  • Familiarity with fast Fourier transform (FFT) techniques
  • Basic knowledge of Power Spectrum analysis
  • Experience with MatLab for data visualization
NEXT STEPS
  • Research the principles of capacitive microphone design and functionality
  • Learn about implementing fast Fourier transform (FFT) in MatLab
  • Explore the concept of Power Spectrum and its significance in frequency analysis
  • Investigate the effects of microphone placement on sound wave interference patterns
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Acoustic engineers, audio technicians, researchers in sound analysis, and anyone interested in measuring frequency responses in noise environments.

Bert Rackett
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I would like to measure the relative amplitudes (energy levels) of several specific
frequencies in a noise field. I thought of attaching capacitive microphones to
tubes that would resonate at those frequencies. I've visited hundreds of web sites
that invariably give equations for frequencies and resonant points, but say nothing
about the amplitude domain unless they're talking about musical instruments.
How much larger will my response be in my tube? How large are the harmonic
responses? I have several texts, but they speak qualitatively about resonances and
not quantitatively. can someone point out a text with the math?
Thank you.
Bert Rackett
 
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As long as you're sampling at more than twice the highest frequency you want to detect, can't you just put the signal through a fast Fourier transform? You could use something like MatLab to do this and plot the Power Spectrum quite easily.

It would be interesting to see how the power spectrum changes in relation to the position of your microphone. You might expect to see notches at frequencies with wavelengths that destructively interfere with reflections off of the surrounding walls.
 

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