torch_stft function

Stft

torch_stft(
  input,
  n_fft,
  hop_length = NULL,
  win_length = NULL,
  window = NULL,
  center = TRUE,
  pad_mode = "reflect",
  normalized = FALSE,
  onesided = NULL,
  return_complex = NULL
)

Arguments

• input: (Tensor) the input tensor
• n_fft: (int) size of Fourier transform
• hop_length: (int, optional) the distance between neighboring sliding window frames. Default: NULL (treated as equal to floor(n_fft / 4))
• win_length: (int, optional) the size of window frame and STFT filter. Default: NULL (treated as equal to n_fft)
• window: (Tensor, optional) the optional window function. Default: NULL (treated as window of all $1$ s)
• center: (bool, optional) whether to pad input on both sides so that the $t$-th frame is centered at time $t \times \mbox{hop\_length}$. Default: TRUE
• pad_mode: (string, optional) controls the padding method used when center is TRUE. Default: "reflect"
• normalized: (bool, optional) controls whether to return the normalized STFT results Default: FALSE
• onesided: (bool, optional) controls whether to return half of results to avoid redundancy Default: TRUE
• return_complex: (bool, optional) controls whether to return complex tensors or not.

Short-time Fourier transform (STFT).

Short-time Fourier transform (STFT).

Ignoring the optional batch dimension, this method computes the following
expression:

where $m$ is the index of the sliding window, and $\omega$ is the frequency that $0 \leq \omega < \mbox{n\_fft}$. When onesided is the default value TRUE,

* `input` must be either a 1-D time sequence or a 2-D batch of time
  sequences.

* If `hop_length` is `NULL` (default), it is treated as equal to
  `floor(n_fft / 4)`.

* If `win_length` is `NULL` (default), it is treated as equal to
  `n_fft`.

* `window` can be a 1-D tensor of size `win_length`, e.g., from
  `torch_hann_window`. If `window` is `NULL` (default), it is
  treated as if having \eqn{1} everywhere in the window. If
  \eqn{\mbox{win\_length} < \mbox{n\_fft}}, `window` will be padded on
  both sides to length `n_fft` before being applied.

* If `center` is `TRUE` (default), `input` will be padded on
  both sides so that the \eqn{t}-th frame is centered at time
  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
  begins at time  \eqn{t \times \mbox{hop\_length}}.

* `pad_mode` determines the padding method used on `input` when
  `center` is `TRUE`. See `torch_nn.functional.pad` for
  all available options. Default is `"reflect"`.

* If `onesided` is `TRUE` (default), only values for \eqn{\omega}
  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
  are returned because the real-to-complex Fourier transform satisfies the
  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.

* If `normalized` is `TRUE` (default is `FALSE`), the function
  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.

Returns the real and the imaginary parts together as one tensor of size
\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
batch size of `input`, \eqn{N} is the number of frequencies where
STFT is applied, \eqn{T} is the total number of frames used, and each pair
in the last dimension represents a complex number as the real part and the
imaginary part.

Warning

This function changed signature at version 0.4.1. Calling with the previous signature may cause error or return incorrect result.