blog:2019:0225_fft_scaling_factor
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blog:2019:0225_fft_scaling_factor [2019/02/25 19:00] – created davek | blog:2019:0225_fft_scaling_factor [2019/03/08 04:47] – davek | ||
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====== FFT scaling factor ====== | ====== FFT scaling factor ====== | ||
+ | What's often confusing about the [[https:// | ||
+ | When trying to understand this, the uninitiated user will google something like "FFT scale factor" | ||
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+ | But really, these users are trying to figure out **why**, not how. I'm mean, isn't that wrong? **Why** is it N times larger? Shouldn' | ||
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+ | ====== ====== | ||
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+ | The answer is yes, sort of. First off, remember what the first " | ||
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+ | If you really do care about the absolute power in the frequency-domain bins, you might want to divide by N. Maybe you care about the total energy. Oh wait, you should sum the bins, then divide by N one time! Now it's faster for you, too. Maybe you only care about the energy of the strongest bin. Just divide that one bin by N and ignore the others! | ||
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+ | As you may realize, even many people who do care about the absolute power, don't care about it to the degree of dividing by N for every single bin. | ||
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+ | So *why* do FFT implementations seem to be off by a factor of N? It's for performance - the FFT is an intermediate result and you should divide by N if and when you need it. | ||
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+ | How is an FFT implementation off by N? Google "FFT scale factor" | ||
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blog/2019/0225_fft_scaling_factor.txt · Last modified: 2022/10/20 16:17 by davek