tuneR: audspec – R documentation

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audspec

Frequency band conversion

Description

Perform critical band analysis (see PLP), which means the reduction of the fourier frequencies of a signal's powerspectrum to a reduced number of frequency bands in an auditory frequency scale.

Usage

audspec(pspectrum, sr = 16000, nfilts = ceiling(hz2bark(sr/2)) + 1, 
    fbtype = c("bark", "mel", "htkmel", "fcmel"), minfreq = 0, 
    maxfreq = sr/2, sumpower = TRUE, bwidth = 1)

Arguments

`pspectrum`	Output of `powspec`, matrix with the powerspectrum of each time frame in its columns.
`sr`	Sample rate of the original recording.
`nfilts`	Number of filters/frequency bins in the auditory frequency scale.
`fbtype`	Used auditory frequency scale.
`minfreq`	Lowest frequency.
`maxfreq`	Highest frequency.
`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.
`bwidth`	Modify the width of the frequency bands.

Value

`aspectrum`	Matrix with the auditory spectrum of each time frame in its columns.
`wts`	Weight matrix for the frequency band conversion.

Author(s)

Sebastian Krey krey@statistik.tu-dortmund.de

References

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

Examples

testsound <- normalize(sine(400) + sine(1000) + square(250), "16")
  pspectrum <- powspec(testsound@left, testsound@samp.rate)
  aspectrum <- audspec(pspectrum, testsound@samp.rate)

tuneR

Analysis of Music and Speech

v1.3.3

GPL-2 | GPL-3

Authors

Uwe Ligges <ligges@statistik.tu-dortmund.de> with contributions from Sebastian Krey, Olaf Mersmann, Sarah Schnackenberg, Guillaume Guenard, Andrea Preusser, Anita Thieler, Johanna Mielke and Claus Weihs, as well as code fragments and ideas from the former package 'sound' by Matthias Heymann and functions from 'rastamat' by Daniel P. W. Ellis. The included parts of the libmad MPEG audio decoder library are authored by Underbit Technologies.

Initial release

2018-07-03