torch_stft: Stft

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Stft

Description

Stft

Usage

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:

X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}% \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ % \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),

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.

torch documentation built on June 7, 2023, 6:19 p.m.