快速傅里叶变换(FFT)是一个高度optimized implementation of the discrete Fourier transform (DFT), which convert discrete signals from the time domain to the frequency domain. FFT computations provide information about the frequency content, phase, and other properties of the signal.
蓝色鲸鱼呻吟音频信号使用FFT分解成其频率分量。（看Matlab代码示例的）
流行的FFT算法包括Cooley-Tukey算法，主要因子FFT算法和Rader的FFT算法。最常用的FFT算法是Cooley-Tukey算法，其将大DFT降低到较小的DFT中以增加计算速度并降低复杂性。FFT在许多领域具有应用。
FFT应用程序
In signal processing, FFT forms the basis of frequency domain analysis (spectral analysis) and is used for signal filtering, spectral estimation, data compression, and other applications. Variations of the FFT such as the short-time Fourier transform also allow for simultaneous analysis in time and frequency domains. These techniques can be used for a variety of signals such as audio and speech, radar, communication, and other sensor data signals. FFT is also sometimes used as an intermediate step for more complex signal processing techniques.
在图像处理中，FFT用于过滤和图像压缩。FFT也用于物理和数学以解决部分微分方程（PDE）。
持久性频谱那a type of time-frequency view, that can be used for spectral analysis of signals. (SeeMATLAB中的时频函数的）
Run FFT Examples in MATLAB Online
Hardware Implementation of FFT
在可编程逻辑设备上实现FFT并不像软件实现那么简单。工程权衡的决定不正确，如速度和准确性或低效代码可能会影响应用的质量和性能。使用MATLAB和SIMULINK代码生金宝app成工具，可以轻松地在各种硬件设备上实现FFT，从诸如ARM到更专业的设备，例如FPGA的通用处理器。
More About FFT
Learn from experts about the history and uses of FFT.
