melfcc: MFCC Calculation

In tuneR: Analysis of Music and Speech

MFCC Calculation

Description

Calculate Mel-frequency cepstral coefficients.

Usage

melfcc(samples, sr = [e-mail], wintime = 0.025, 
    hoptime = 0.01, numcep = 12, lifterexp = 0.6, htklifter = FALSE,
    sumpower = TRUE, preemph = 0.97, dither = FALSE,
    minfreq = 0, maxfreq = sr/2, nbands = 40, bwidth = 1, 
    dcttype = c("t2", "t1", "t3", "t4"), 
    fbtype = c("mel", "htkmel", "fcmel", "bark"), usecmp = FALSE, 
    modelorder = NULL, spec_out = FALSE, frames_in_rows = TRUE)

Arguments

samples	Object of Wave-class or WaveMC-class. Only the first channel will be used.
sr	Sampling rate of the signal.
wintime	Window length in sec.
hoptime	Step between successive windows in sec.
numcep	Number of cepstra to return.
lifterexp	Exponent for liftering; 0 = none.
htklifter	Use HTK sin lifter.
sumpower	If sumpower = TRUE the frequency scale transformation is based on the powerspectrum, if sumpower = FALSE it is based on its squareroot (absolute value of the spectrum) and squared afterwards.
preemph	Apply pre-emphasis filter [1 -preemph] (0 = none).
dither	Add offset to spectrum as if dither noise.
minfreq	Lowest band edge of mel filters (Hz).
maxfreq	Highest band edge of mel filters (Hz).
nbands	Number of warped spectral bands to use.
bwidth	Width of spectral bands in Bark/Mel.
dcttype	Type of DCT used - 1 or 2 (or 3 for HTK or 4 for feacalc).
fbtype	Auditory frequency scale to use: "mel", "bark", "htkmel", "fcmel".
usecmp	Apply equal-loudness weighting and cube-root compression (PLP instead of LPC).
modelorder	If modelorder > 0, fit a linear prediction (autoregressive-) model of this order and calculation of cepstra out of lpcas.
spec_out	Should matrices of the power- and the auditory-spectrum be returned.
frames_in_rows	Return time frames in rows instead of columns (original Matlab code).

Details

Calculation of the MFCCs imlcudes the following steps:

1. Preemphasis filtering

2. Take the absolute value of the STFT (usage of Hamming window)

3. Warp to auditory frequency scale (Mel/Bark)

4. Take the DCT of the log-auditory-spectrum

5. Return the first ‘ncep’ components

Value

cepstra	Cepstral coefficients of the input signal (one time frame per row/column)
aspectrum	Auditory spectrum (spectrum after transformation to Mel/Bark scale) of the signal
pspectrum	Power spectrum of the input signal.
lpcas	If modelorder > 0, the linear prediction coefficients (LPC/PLP).

Note

The following non-default values nearly duplicate Malcolm Slaney's mfcc (i.e.

melfcc(d, 16000, wintime=0.016, lifterexp=0, minfreq=133.33, 
       maxfreq=6855.6, sumpower=FALSE)

=~= log(10) * 2 * mfcc(d, 16000) in the Auditory toolbox for Matlab).

The following non-default values nearly duplicate HTK's MFCC (i.e.

melfcc(d, 16000, lifterexp=22, htklifter=TRUE, nbands=20, maxfreq=8000, 
    sumpower=FALSE, fbtype="htkmel", dcttype="t3")

=~= 2 * htkmelfcc(:,[13,[1:12]]) where HTK config has ‘PREEMCOEF = 0.97’, ‘NUMCHANS = 20’, ‘CEPLIFTER = 22’, ‘NUMCEPS = 12’, ‘WINDOWSIZE = 250000.0’, ‘USEHAMMING = T’, ‘TARGETKIND = MFCC_0’).

For more detail on reproducing other programs' outputs, see https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/mfccs.html

Author(s)

Sebastian Krey [e-mail]

References

Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/

Examples

  testsound <- normalize(sine(400) + sine(1000) + square(250), "16")
  m1 <- melfcc(testsound)

  #Use PLP features to calculate cepstra and output the matrices like the
  #original Matlab code (note: modelorder limits the number of cepstra)
  m2 <- melfcc(testsound, numcep=9, usecmp=TRUE, modelorder=8, 
    spec_out=TRUE, frames_in_rows=FALSE)

tuneR documentation built on May 29, 2024, 11:28 a.m.