soundgen (version 3.0.0)

invertSpectrogram: Invert spectrogram

Description

Transforms a spectrogram into a time series with inverse STFT. The problem is that an ordinary spectrogram preserves only the magnitude (modulus) of the complex STFT, while the phase is lost, and without phase it is impossible to reconstruct the original audio accurately. So there are a number of algorithms for "guessing" the phase that would produce an audio whose magnitude spectrogram is very similar to the target spectrogram. Useful for certain filtering operations that modify the magnitude spectrogram followed by inverse STFT, such as filtering in the spectro-temporal modulation domain.

Usage

invertSpectrogram(
  spec,
  samplingRate = NULL,
  windowLength,
  step,
  overlap = NULL,
  wn,
  specScale = c("spec", "power", "log", "dB"),
  initialPhase = c("spsi", "random", "zero"),
  nIter = 50,
  normalize = TRUE,
  play = FALSE,
  verbose = FALSE,
  plotError = TRUE
)

Value

Reconstructed audio as a numeric vector.

Arguments

spec

the spectrogram that is to be transform to a time series: numeric matrix of real value (no phase) with frequency bins in rows (kHz) and time frames in columns (ms)

samplingRate

sampling rate (not needed if the spectrogram has rownames corresponding to frequency bins with the last one at Nyquist = samplingRate / 2)

windowLength, step, overlap, wn

STFT parameters used to create the original spectrogram; make sure zp = 0

specScale

the scale of target spectrogram: 'spec' = untransformed amplitude spectrum, 'power' = power spectrum, 'log' = log-transformed, 'dB' = in decibels

initialPhase

initial phase estimate: "spsi" (default) = single-pass spectrogram inversion (Beauregard et al., 2015); "zero" = set all phases to zero; "random" = Gaussian noise

nIter

the number of iterations of the GL algorithm (Griffin & Lim, 1984), 0 = don't run

normalize

if TRUE, normalizes the output to range from -1 to +1

play

if TRUE, plays back the reconstructed audio

verbose

if TRUE, prints estimated time left every 10% of GL iterations

plotError

if TRUE, plots the error during GL iterations and prints the final reconstruction error (useful for choosing nIter)

Details

Algorithm: takes the spectrogram, makes an initial guess at the phase (zero, noise, or a more intelligent estimate by the SPSI algorithm), fine-tunes over nIter iterations with the GL algorithm, reconstructs the complex spectrogram using the best phase estimate, and performs inverse STFT. The single-pass spectrogram inversion (SPSI) algorithm is implemented as described in Beauregard et al. (2015) following the python code at https://github.com/lonce/SPSI_Python. The Griffin-Lim (GL) algorithm is based on Griffin & Lim (1984).

References

• Griffin, D., & Lim, J. (1984). Signal estimation from modified short-time Fourier transform. IEEE Transactions on Acoustics, Speech, and Signal Processing, 32(2), 236-243.

• Beauregard, G. T., Harish, M., & Wyse, L. (2015, July). Single pass spectrogram inversion. In 2015 IEEE International Conference on Digital Signal Processing (DSP) (pp. 427-431). IEEE.

See Also

spectrogram filterSoundByMS

Examples

# Create a spectrogram. NB: do NOT use zero-padding
samplingRate = 16000
windowLength = 40
step = 5
wn = 'gaussian'
# NB: the more detailed a spectrogram, the more precisely it can be inverted,
# so a relatively long window AND a short step make for best results

s = soundgen(samplingRate = samplingRate, addSilence = 50)
spec = spectrogram(s, samplingRate = samplingRate,
  wn = wn, windowLength = windowLength, step = step,
  zp = 0,  # otherwise it changes the window length and messes up istft
  padWithSilence = FALSE, output = 'original')

# Invert the spectrogram, attempting to guess the phase
# Note that we need to know the original windowLength, step, and wn
# (i.e., you have to know how the spectrogram was created)
s_new = invertSpectrogram(spec, samplingRate = samplingRate,
  windowLength = windowLength, step = step, wn = wn,
  initialPhase = 'spsi', nIter = 50, play = FALSE)

# Verify the quality of audio reconstruction
# playme(s, samplingRate); playme(s_new, samplingRate)

if (FALSE) {
# to improve the quality of reconstruction, increase the number of iterations
s_new = invertSpectrogram(spec, samplingRate = samplingRate,
  windowLength = windowLength, step = step, wn = wn,
  initialPhase = 'spsi', nIter = 500, play = FALSE)
playme(s, samplingRate); playme(s_new, samplingRate)
spectrogram(s, samplingRate)
spectrogram(s_new, samplingRate)
}