FFT 数据看起来很奇怪
这是我的结果的简短视频 声音很可怕，但请耐心听我说。
对我来说，这看起来有点奇怪。前几个垃圾箱的巨大峰值是怎么回事？非线性是怎么回事？
我正在绘制实部和虚部平方和的平方根。我尝试在此基础上使用对数，但基线发生了很大的移动，也就是说，频谱没有保持在屏幕的中心。
如果您能指出正确的方向，我将不胜感激！
Here is a short video of my results
The sound is horrible, but please bear with me.
To me, that looks a little strange. Whats with the massive spike on the first few bins? Whats the deal with the non-linearity?
I'm plotting the square root of the sum of the squares of the real and imaginary parts. I've tried using a logarithm on top of that, but I get a lot of movement of the baseline, that is, the spectrum doesn't stay centered in the screen.
If you can point me in the right direction, I'd appreciate it!
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实际上你发布的 FFT 结果看起来相当正常。
您有一个巨大的 DC 偏移，它会影响 FFT 结果的前几个 bin。
时域信号中还存在谐波或谐波失真，从而在 FFT 结果中的正弦波频率扫描峰值上方看到一系列泛音。
您存在混叠，可能是由于采样前缺乏足够的低通滤波，导致这些泛音完全覆盖或围绕 FFT 结果另一侧的复共轭峰的频率响应，因此看起来像是向另一个方向移动。
由于您的频率扫描产生的频率不在 FFT bin 中心，并且您没有使用任何类型的“平顶”窗口，这将导致您在主信号峰值中看到轻微的 Sinc 响应幅度扇形 。
由于您似乎使用了矩形窗口，因此只要频率峰值不在 FFT bin 中心，您就会有进一步的 Sinc 响应幅度“凸起”从主峰辐射到侧面。有时称为“频谱泄漏”。
Actually your posted FFT results looks fairly normal.
You have a huge DC offset which affects the first few bins of the FFT result.
You also have harmonics or harmonic distortion in your time-domain signal creating the train of overtones you see above your sine-wave frequency sweep peak in the FFT result.
You have aliasing, possibly from a lack of sufficient low pass filtering before sampling, causing these overtones to wrap completely across or around the frequency response from the complex conjugate peak on the other side of the FFT result, and thus appear to be moving the other direction.
Since your frequency sweep produces frequencies that are not at the FFT bin centers, and you are not using any kind of "flat-top" window, this will cause the slight Sinc response magnitude scalloping you are seeing in the main signal peak.
Since you appear to have used a rectangular window, you have further Sinc response magnitude "bumps" radiating to the side from you main peak whenever a frequency peak isn't at an FFT bin center. Sometimes called "spectral leakage".
第一个 bin 是 DC。如果您的样本窗口有直流偏置（换句话说，它的平均值不为 0），那么直流箱中将会有大量能量。这可能就像传递以 2^{n-1} 为中心的无符号数据而不是以 0 为中心的有符号数据一样简单。
我不确定您的文本或视频中的“非线性”是什么意思。
The first bin is DC. If your sample window has a DC bias (in other words it has an average that's not 0) then there will be a lot of energy in the DC bin. This could be as simple as passing in unsigned data centered around 2^{n-1} rather than signed data centered around 0.
I'm not sure what you mean by "nonlinearity" from your text or your video.