soundgen (version 3.0.0)

noiseRemoval: Noise removal

Description

Removes noise by log-spectral subtraction. If a recording is affected by a steady noise with a relatively stable amplitude and spectrum (e.g., microphone hiss, crickets, MRI buzz, etc.), its log-spectrum can be simply subtracted from that of the signal. Algorithm: STFT to produce a log-spectrogram, subtract normalized noise spectrum, iSTFT to reconstitute the signal. Most of the work is done by addFormants.

Usage

noiseRemoval(
  x,
  samplingRate = NULL,
  from = NULL,
  to = NULL,
  noise,
  dB = 6,
  specificity = 1,
  windowLength = 50,
  step = NULL,
  overlap = 75,
  dynamicRange = 120,
  normalize = c("orig", "max", "none"),
  reportEvery = NULL,
  cores = 1,
  play = FALSE,
  saveAudio = FALSE,
  plot = FALSE,
  savePlots = FALSE,
  embed = FALSE,
  width = 900,
  height = 500,
  units = "px",
  res = NA,
  ...
)

Value

The denoised audio as a numeric vector (multiple inputs return a list).

Arguments

x

path to a folder, one or more wav or mp3 files c('file1.wav', 'file2.mp3'), Wave object, numeric vector, or a list of Wave objects or numeric vectors

samplingRate

sampling rate of x (only needed if x is a numeric vector)

from, to

if specified (in seconds), only this section of input is denoised

noise

a numeric vector of length two specifying the location of pure noise in input audio (in s); a matrix representing pure noise as a spectrum with frequency bins in rows; path to file, Wave object, or numeric vector (with the same sampling rate as x) representing pure noise

dB

controls the amount of noise removal: larger values are more aggressive

specificity

a way to sharpen or blur the noise spectrum (we take noise spectrum ^ specificity) : 1 = no change, >1 = sharper (the loudest noise frequencies are preferentially removed), <1 = blurred (even quiet noise frequencies are removed)

windowLength

length of the analysis window, ms

step

step between successive windows, ms; if provided, overrides overlap; because digital audio is sampled at discrete time intervals of 1/samplingRate, the actual step and thus the time stamps of STFT frames may be slightly different - e.g., 24.98866 instead of 25.0 ms

overlap

overlap between successive windows, %

dynamicRange

regions under -dynamicRange dB are treated as silent

normalize

"orig" = same as input (default), "max" = maximum possible peak amplitude for the given input scale, "none" = no normalization

reportEvery

when processing multiple inputs, report estimated time left every reportEvery iterations (NULL = default, NA = don't report); see reportTime

cores

number of cores for parallel processing

play

if TRUE, plays the output audio using the default player on your system. If a character string, it is passed to playme as the name of the player to use (e.g. 'aplay', 'play', 'vlc'). In case of errors, try setting another default player for playme

saveAudio

if TRUE, saves the processed audio in a subdirectory named after the function and created in the input directory (if input is a file or folder) or in the working directory

plot

if TRUE, produces a plot of the results

savePlots

if TRUE, creates a subdirectory in the input directory (if input is a file or folder) or in the working directory (if input is a vector etc), named after the function (eg "spectrogram/"). All plots and audio files (if any) are saved in this new directory. If there are multiple inputs, an html notebook is also created for easy viewing and listening

embed

if TRUE and savePlots is set and there are multiple inputs, all saved images and audio (if any) are embedded in the exported html notebook for easy sharing; if FALSE, the html file links to separate images and audio files (but separate files are still saved). NB: for this to work, package "base64enc" must be installed

width, height, units, res

graphical parameters for saving plots passed to png

...

extra graphical parameters passed to spectrogram

See Also

addFormants

Examples

s = soundgen(noise = list(time = c(-100, 400), value = -10),
  formantsNoise = list(f1 = list(freq = 3000, width = 25)),
  addSilence = 50, temperature = .001, plot = TRUE)
# Option 1: use part of the recording as noise profile
s1 = noiseRemoval(s, samplingRate = 16000, noise = c(0.05, 0.15),
  dB = 40, plot = TRUE)

if (FALSE) {
# Option 2: use a separate recording as noise profile
noise = soundgen(pitch = NA, noise = 0,
  formantsNoise = list(f1 = list(freq = 3000, width = 25)))
spectrogram(noise, 16000)
s2 = noiseRemoval(s, samplingRate = 16000, noise = noise,
  dB = 40, plot = TRUE)

# Option 3: provide noise spectrum as a matrix
spec_noise = spectrogram(
      noise, samplingRate = 16000,
      output = 'original', plot = FALSE)
s3 = noiseRemoval(s, samplingRate = 16000, noise = spec_noise,
  dB = 40, plot = TRUE)

# play with gain and specificity
s4 = noiseRemoval(s, samplingRate = 16000, noise = c(0.05, 0.15),
  dB = 60, specificity = 2, plot = TRUE)

# remove noise only from a section of the audio
s5 = noiseRemoval(s, samplingRate = 16000, from = .3, to = .5,
  noise = c(0.05, 0.15), dB = 60, plot = TRUE)
}