Identifying the harmonics on FFT

In summary, FFT (Fast Fourier Transform) is a mathematical algorithm used to analyze a signal and identify its frequency components. It can be used to identify the harmonics present in a signal by converting the signal from its time domain to its frequency domain. A harmonic is a frequency component that is a multiple of the fundamental frequency of a signal, and FFT can identify and isolate these harmonics in a signal. The results of an FFT analysis typically include a graph or plot that shows the frequency components of a signal, with the peaks representing the harmonics present. However, FFT may not be able to accurately identify all harmonics depending on the resolution and sampling rate of the signal. When using FFT to identify harmonics, precautions should be taken to ensure
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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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  • #2
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
 

1. What is FFT and how is it used to identify harmonics?

FFT (Fast Fourier Transform) is a mathematical algorithm used to analyze a signal and identify its frequency components. It can be used to identify the harmonics present in a signal by converting the signal from its time domain to its frequency domain.

2. What is a harmonic and how does it relate to FFT?

A harmonic is a frequency component that is a multiple of the fundamental frequency of a signal. In other words, it is a frequency that is a whole number multiple of the lowest frequency present in the signal. FFT can identify and isolate these harmonics in a signal, providing valuable information about the underlying frequency components.

3. How do I interpret the results of an FFT analysis for identifying harmonics?

The results of an FFT analysis typically include a graph or plot that shows the frequency components of a signal. The peaks on the graph represent the harmonics present in the signal, with the tallest peak representing the fundamental frequency. The distance between each peak can also provide information about the strength and spacing of the harmonics.

4. Can FFT accurately identify all harmonics in a signal?

FFT is a powerful tool for identifying harmonics, but it is not perfect. It is limited by the resolution and sampling rate of the signal being analyzed. If the signal has a high sampling rate and a large range of frequencies, FFT may not be able to accurately identify all harmonics. In these cases, a higher resolution FFT or a different analysis method may be needed.

5. Are there any precautions I should take when using FFT to identify harmonics?

When using FFT to identify harmonics, it is important to ensure that the signal being analyzed is clean and free of noise or interference. Noise can distort the results and make it difficult to accurately identify harmonics. It is also important to properly select the FFT parameters, such as window size and frequency resolution, to ensure the best possible analysis results.

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