Identifying the harmonics on FFT

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SUMMARY

The discussion focuses on accurately identifying the fundamental harmonic in frequency analysis, particularly when peaks of similar amplitudes occur at closely spaced frequencies. It confirms that the second, third, and fourth harmonics are indeed multiples of the fundamental frequency, regardless of their lower amplitudes compared to nearby frequencies. The Average Magnitude Difference Function (AMDF) and the Average Squared Difference Function are highlighted as effective tools for determining the fundamental frequency in musical signal analysis.

PREREQUISITES
  • Understanding of Fast Fourier Transform (FFT) principles
  • Familiarity with harmonic analysis in signal processing
  • Knowledge of Average Magnitude Difference Function (AMDF)
  • Experience with Average Squared Difference Function in waveform analysis
NEXT STEPS
  • Research the implementation of Fast Fourier Transform (FFT) in Python
  • Explore techniques for harmonic analysis in MATLAB
  • Learn about the application of autocorrelation in signal processing
  • Investigate the differences between AMDF and autocorrelation methods
USEFUL FOR

Audio engineers, signal processing researchers, and anyone involved in musical signal analysis will benefit from this discussion.

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How do you accurately identify the fundamental harmonic if you get a peak of similar amplitudes at frequencies that are really close to each other.

And furthermore, once you identify the fundamental harmonic, are the second, third fourth harmonics just multiples of the fundamental frequency? Even if the amplitudes of these harmonics are lower than the amplitudes of nearby frequencies?
 
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in the musical signal analysis sub-discipline, we use something called the Average Magnitude Difference Function (AMDF) or Average Squared Difference Function or autocorrelation to determine the fundamental frequency of a waveform
 

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