melfilterbank: Mel-filter bank for MFCC computation

In seewave: Sound Analysis and Synthesis

Mel-filter bank for MFCC computation

Description

This functions returns graphically and numerically the Mel-filters used to compute MFCC.

Usage

melfilterbank(f = 44100, wl = 1024,
minfreq = 0, maxfreq = f/2, m = 20,
palette, alpha = 0.5, plot = FALSE)

Arguments

f	sammpling frequency (in Hz).
wl	the Fourier window length (in number of samples).
minfreq	the minimum (or lower) frequency of the filter bank (in Hz).
maxfreq	the maximum (or upper) frequency of the filter bank (in Hz).
m	the total number of filters.
palette	an optional colour palette if plot is TRUE.
alpha	alpha-transparency when a colour palette is used.
plot	if TRUE all filters are displayed in a single plot.

Value

A list of 3 items:

central.freq	the kHz central frequencies of the filters,
freq	the kHz frequency scale,
amp	the amplitude of the filters, scaled between 0 and 1.

Note

These triangular filters are used for computing MFCCs.

Author(s)

Jerome Sueur

References

Sharan RV & Moir TJ (2016) Applications and advancements in automatic sound recognition. Neurocomputing.

See Also

mel, gammatone

Examples

## default values
melfilterbank(plot=TRUE)
## with color surfaces
melfilterbank(palette=cm.colors, plot=TRUE)
## values changed
res <- melfilterbank(f=16000, wl=512, minfreq=300, plot=TRUE)
## plot the 1st filter only
plot(res$freq, res$amp[,1], type="l", xlab="Frequency (kHz)", ylab="Amplitude")
## plot the last filter only
plot(res$freq, res$amp[,ncol(res$amp)], type="l", xlab="Frequency (kHz)", ylab="Amplitude")
## get the kHz central frequencies of the succesive filters
res$central.freq

seewave documentation built on Oct. 19, 2023, 5:07 p.m.