Next  |  Prev  |  Up  |  Top  |  Index  |  JOS Index  |  JOS Pubs  |  JOS Home  |  Search

Summary of STFT Computation Using FFTs

  1. Read $ M$ samples of the input signal $ x$ into a local buffer of length $ N\geq M$ which is initially zeroed

    $\displaystyle \tilde{x}_m(n) \isdef x(n+mR), \qquad n=-M_h,
\ldots\,,-1,0,1,\ldots\,,
M_h
$

    We call $ x_m$ the $ m$ th frame of the input signal, and $ \tilde{x}_m=x(n+mR)$ the $ m$ th time normalized input frame (time-normalized by translating it to time zero). The frame length is $ M\isdef 2M_h+1$ , which we assume to be odd for reasons to be discussed later. The time advance $ R$ (in samples) from one frame to the next is called the hop size or step size.

  2. Multiply the data frame pointwise by a length $ M$ spectrum analysis window $ w(n), n=-M_h,\ldots\,,M_h$ to obtain the $ m$ th windowed data frame (time normalized):

    $\displaystyle \tilde{x}_m^w(n) \isdef \tilde{x}_m(n) w(n), \qquad n=-M_h,\ldots\,,M_h
$

  3. Extend $ \tilde{x}_m^w$ with zeros on both sides to obtain a zero-padded frame:

    $\displaystyle \tilde{x}_m^{w,z}(n) \isdef \left\{\begin{array}{ll} \tilde{x}_m^w(n), & \left\vert n\right\vert\leq M_h\isdef {\frac{M-1}{2}} \\ [5pt] 0, & M_h< n \leq {\frac{N}{2}}-1 \\ [5pt] 0, & -{\frac{N}{2}}\leq n < -M_h \\ \end{array} \right.$ (8.5)

    where $ N$ is chosen to be a power of two larger than $ M$ . The number $ N/M$ is the zero-padding factor. As discussed in §2.5.3, the zero-padding factor is the interpolation factor for the spectrum, i.e., each FFT bin is replaced by $ N/M$ bins, interpolating the spectrum using ideal bandlimited interpolation [264], where the ``band'' in this case is the $ M$ -sample nonzero duration of $ \tilde{x}_m^w$ in the time domain.

  4. Take a length $ N$ FFT of $ \tilde{x}_m^{w,z}$ to obtain the time-normalized, frequency-sampled STFT at time $ m$ :

    $\displaystyle \tilde{X}_m^{w,z}(\omega_k )=\sum _{n=-N/2}^{N/2-1} \tilde{x}_m^{w,z}(n) e^{-j\omega_k n T}$ (8.6)

    where $ \omega_k = 2\pi k f_s / N $ , and $ f_s=1/T$ is the sampling rate in Hz. As in any FFT, we call $ k$ the bin number.

  5. If needed, time normalization may be removed using a linear phase term to yield the sampled STFT:

    $\displaystyle X^{w,z}_m(\omega_k) = e^{-j\omega_k mR}\tilde{X}_m^{w,z}(\omega_k )$ (8.7)

    The (continuous-frequency) STFT may be approached arbitrarily closely by using more zero padding and/or other interpolation methods.

    Note that there is no irreversible time-aliasing when the STFT frequency axis $ \omega$ is sampled to the points $ \omega_k$ , provided the FFT size $ N$ is greater than or equal to the window length $ M$ .


Next  |  Prev  |  Up  |  Top  |  Index  |  JOS Index  |  JOS Pubs  |  JOS Home  |  Search

[How to cite this work]  [Order a printed hardcopy]  [Comment on this page via email]

``Spectral Audio Signal Processing'', by Julius O. Smith III, W3K Publishing, 2011, ISBN 978-0-9745607-3-1.
Copyright © 2016-07-18 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University
CCRMA