Contents
What is FFT spectra?
The spectrum is the basic measurement of an FFT analyzer. It is simply the complex FFT. Normally, the magnitude of the spectrum is displayed. The magnitude is the square root of the FFT times its complex conjugate. (Square root of the sum of the real (sine) part squared and the imaginary (cosine) part squared.)
Why do we need octave bands?
When the noise is measured using a sound level meter, the overall noise level is 113dB(A). By using 1:1 octave band filters, we can break the noise from each machine into its component parts and it is very clear that the turbine is producing much more low frequency noise than the compressor.
How to derive fractional octave spectra from the FFT?
For example, the FFT of a 16,384 point acquisition acquired at 48 kHz sample rate, the resulting FFT has 8192 bins and each bin has a width of about 2.93 Hz (48,000/16,384). If an FFT spectrum has sufficient resolution, a fractional octave spectrum can be derived from it.
How to get the third octave frequency spectrum?
Using fft I get the expected result: Multiples of the fundamental frequency are the relevant frequencies in the spectrum. To get the third-octave frequency spectrum I wanted to use python acoustics, but the result of the function bandpass_third_octaves is not what I expected.
What to do if you need lower fractional octave frequency?
If you need a lower fractional octave frequency than the one displayed, (1) increase the FFT length, (2) lower the sample rate, or do both . Similarly, if you don’t need as low a frequency as displayed, you can decrease the FFT length to reduce the processing time.
What is the gain curve for a 1 / 3 octave filter?
For 1/3-octaves (b = 3), this gain curve meets the Class 0 tolerance requirements of IEC 61260 for 1/3-octave filters. where b = bandwidth designator (1 for full octave, 3 for 1/3-octave,… etc.) f = frequency fm = the mid-band frequency of the 1/b-octave filter g = the filter gain