soundgen (version 1.5.0)

generateNoise: Generate noise

Description

Generates noise of length len and with spectrum defined by linear decay of rolloffNoise dB/kHz above noiseFlatSpec Hz OR by a specified filter spectralEnvelope. This function is called internally by soundgen, but it may be more convenient to call it directly when synthesizing non-biological noises defined by specific spectral and amplitude envelopes rather than formants: the wind, whistles, impact noises, etc. See fart and beat for similarly simplified functions for tonal non-biological sounds.

Usage

generateNoise(len, rolloffNoise = 0, noiseFlatSpec = 1200,
  spectralEnvelope = NULL, noise = NULL, temperature = 0.1,
  attackLen = 10, windowLength_points = 1024, samplingRate = 16000,
  overlap = 75, dynamicRange = 80, play = FALSE)

Arguments

len

length of output

rolloffNoise

linear rolloff of the excitation source for the unvoiced component, dB/kHz (anchor format)

noiseFlatSpec

keeps noise spectrum flat to this frequency, Hz

spectralEnvelope

(optional): as an alternative to using rolloffNoise, we can provide the exact filter - a vector of non-negative numbers specifying the power in each frequency bin on a linear scale (interpolated to length equal to windowLength_points/2). A matrix specifying the filter for each STFT step is also accepted. The easiest way to create this matrix is to call soundgen:::getSpectralEnvelope or to use the spectrum of a recorded sound

noise

loudness of turbulent noise (0 dB = as loud as voiced component, negative values = quieter) such as aspiration, hissing, etc (anchor format)

temperature

hyperparameter for regulating the amount of stochasticity in sound generation

attackLen

duration of fade-in / fade-out at each end of syllables and noise (ms): a vector of length 1 (symmetric) or 2 (separately for fade-in and fade-out)

windowLength_points

the length of fft window, points

samplingRate

sampling frequency, Hz

overlap

FFT window overlap, %. For allowed values, see istft

dynamicRange

dynamic range, dB. Harmonics and noise more than dynamicRange under maximum amplitude are discarded to save computational resources

play

if TRUE, plays the synthesized sound using the default player on your system. If character, passed to play as the name of player to use, eg "aplay", "play", "vlc", etc. In case of errors, try setting another default player for play

Details

Algorithm: paints a spectrogram with desired characteristics, sets phase to zero, and generates a time sequence via inverse FFT.

Examples

# NOT RUN {
# .5 s of white noise
samplingRate = 16000
noise1 = generateNoise(len = samplingRate * .5,
  samplingRate = samplingRate)
# playme(noise1, samplingRate)
# seewave::meanspec(noise1, f = samplingRate)

# Percussion (run a few times to notice stochasticity due to temperature = .25)
noise2 = generateNoise(len = samplingRate * .15, noise = c(0, -80),
  rolloffNoise = c(4, -6), attackLen = 5, temperature = .25)
noise3 = generateNoise(len = samplingRate * .25, noise = c(0, -40),
  rolloffNoise = c(4, -20), attackLen = 5, temperature = .25)
# playme(c(noise2, noise3), samplingRate)

# }
# NOT RUN {
playback = c(TRUE, FALSE, 'aplay', 'vlc')[2]
# 1.2 s of noise with rolloff changing from 0 to -12 dB above 2 kHz
noise = generateNoise(len = samplingRate * 1.2,
  rolloffNoise = c(0, -12), noiseFlatSpec = 2000,
  samplingRate = samplingRate, play = playback)
# spectrogram(noise, samplingRate, osc = TRUE)

# Similar, but using the dataframe format to specify a more complicated
# contour for rolloffNoise:
noise = generateNoise(len = samplingRate * 1.2,
  rolloffNoise = data.frame(time = c(0, .3, 1), value = c(-12, 0, -12)),
  noiseFlatSpec = 2000, samplingRate = samplingRate, play = playback)
# spectrogram(noise, samplingRate, osc = TRUE)

# To create a sibilant [s], specify a single strong, broad formant at ~7 kHz:
windowLength_points = 1024
spectralEnvelope = soundgen:::getSpectralEnvelope(
  nr = windowLength_points / 2, nc = 1, samplingRate = samplingRate,
 formants = list('f1' = data.frame(time = 0, freq = 7000,
                                   amp = 50, width = 2000)))
noise = generateNoise(len = samplingRate,
  samplingRate = samplingRate, spectralEnvelope = as.numeric(spectralEnvelope),
  play = playback)
# plot(spectralEnvelope, type = 'l')

# Low-frequency, wind-like noise
spectralEnvelope = soundgen:::getSpectralEnvelope(
  nr = windowLength_points / 2, nc = 1, lipRad = 0,
  samplingRate = samplingRate, formants = list('f1' = data.frame(
    time = 0, freq = 150, amp = 30, width = 90)))
noise = generateNoise(len = samplingRate,
  samplingRate = samplingRate, spectralEnvelope = as.numeric(spectralEnvelope),
  play = playback)

# Manual filter, e.g. for a kettle-like whistle (narrow-band noise)
spectralEnvelope = c(rep(0, 100), 120, rep(0, 100))  # any length is fine
# plot(spectralEnvelope, type = 'b')  # notch filter at Nyquist / 2, here 4 kHz
noise = generateNoise(len = samplingRate, spectralEnvelope = spectralEnvelope,
  samplingRate = samplingRate, play = playback)

# Compare to a similar sound created with soundgen()
# (unvoiced only, a single formant at 4 kHz)
noise_s = soundgen(pitch = NULL,
  noise = data.frame(time = c(0, 1000), value = c(0, 0)),
  formants = list(f1 = data.frame(freq = 4000, amp = 80, width = 20)),
  play = playback)


# Use the spectral envelope of an existing recording (bleating of a sheep)
# (see also the same example with tonal source in ?addFormants)
data(sheep, package = 'seewave')  # import a recording from seewave
sound_orig = as.numeric(sheep@left)
samplingRate = [e-mail]
# playme(sound_orig, samplingRate)

# extract the original spectrogram
windowLength = c(5, 10, 50, 100)[1]  # try both narrow-band (eg 100 ms)
# to get "harmonics" and wide-band (5 ms) to get only formants
spectralEnvelope = spectrogram(sound_orig, windowLength = windowLength,
  samplingRate = samplingRate, output = 'original')
sound_noise = generateNoise(len = length(sound_orig),
  spectralEnvelope = spectralEnvelope, rolloffNoise = 0,
  samplingRate = samplingRate, play = playback)
# playme(sound_noise, samplingRate)

# The spectral envelope is similar to the original recording. Compare:
par(mfrow = c(1, 2))
seewave::meanspec(sound_orig, f = samplingRate, dB = 'max0')
seewave::meanspec(sound_noise, f = samplingRate, dB = 'max0')
par(mfrow = c(1, 1))
# However, the excitation source is now white noise
# (which sounds like noise if windowLength is ~5-10 ms,
# but becomes more and more like the original at longer window lengths)
# }