functional_griffinlim: Griffin-Lim Transformation (functional)

In curso-r/torchaudio: R Interface to 'pytorch''s 'torchaudio'

Griffin-Lim Transformation (functional)

Description

Compute waveform from a linear scale magnitude spectrogram using the Griffin-Lim transformation. Implementation ported from librosa.

Usage

functional_griffinlim(
  specgram,
  window,
  n_fft,
  hop_length,
  win_length,
  power,
  normalized,
  n_iter,
  momentum,
  length,
  rand_init
)

Arguments

specgram	(Tensor): A magnitude-only STFT spectrogram of dimension (..., freq, frames) where freq is n_fft %/% 2 + 1.
window	(Tensor): Window tensor that is applied/multiplied to each frame/window
n_fft	(int): Size of FFT, creates n_fft %/% 2 + 1 bins
hop_length	(int): Length of hop between STFT windows.
win_length	(int): Window size.
power	(float): Exponent for the magnitude spectrogram, (must be > 0) e.g., 1 for energy, 2 for power, etc.
normalized	(bool): Whether to normalize by magnitude after stft.
n_iter	(int): Number of iteration for phase recovery process.
momentum	(float): The momentum parameter for fast Griffin-Lim. Setting this to 0 recovers the original Griffin-Lim method. Values near 1 can lead to faster convergence, but above 1 may not converge.
length	(int or NULL): Array length of the expected output.
rand_init	(bool): Initializes phase randomly if TRUE, to zero otherwise.

Value

tensor: waveform of (..., time), where time equals the length parameter if given.

curso-r/torchaudio documentation built on May 4, 2023, 2:27 a.m.