The Auditory Modeling Toolbox


Models included in AMT

The goal of AMT is to provide a complete set of models that deal with the auditory hearing system. Ranging from the outer ear up to the cortex. On this way there a lot of different steps that can be considered. Maybe you are just interested in one part ofthe modeling chain or in the conbination of only specific parts on this way. To ensure this AMT is build in a modular fashion that allows to use only some parts of the whole chain and combine them in different parts.

The following table gives you an overview of the available models, their position in the auditory processing scheme and their corresponding references.

Note that the table is currently outdated. An update will be done soon, in the meantime, we refer to the online documentation.

If you want to contribute your own model, feel free to contact us.

Description Functions References
Pre-processing
Outer ear
Different head-related transfer function (HRTF) data sets.
At the moment data sets from ... are included.
read_hrir.m
download_hrtfs.m
wierstorf2011hrtf
gardner1995hrtf
kayser2009database
Middle ear
FIR filter approximating the effect of the middle ear
middleearfilter.m goode1994nkf
lopezpoveda2001hnc
Gammatone filters
Gammatone filter bank
Rectangular filters
auditoryfilterbank.m
gammatone.m
may2011gammatone.m
aertsen1980strI
patterson1988efficient
lyon1997all
may2011
Gammatone filters with synthesis option
These Gammatone filters allow a reconstruction of the filtered signal.
gfb_analyzer_new.m
gfb_analyzer_process.m
gfb_synthesizer_new.m
gfb_synthesizer_process.m
demo_hohmannfilterbank.m
hohmann2002
Dual Resonance Non-Linear (DRNL) filterbank
DRNL models the basilar membrane non-linearity
drnl.m
demo_drnl.m
meddis2001computational
lopezpoveda2001hnc
Verhulst model
Non-linear cochlea model
verhulst2012.m verhulst2012
FFT based filter bank
Constant-Q filter bank by averaging the magnitude bins
cqdft.m langendijk2002contribution
Third octave filter bank
???
Is this a filter bank at all?
thirdoctrmsanalysis24.m ???
Inner hair cells
Inner hair cells are modelled by an half-wave rectification followed by low pass filtering
ihcenvelope.m bernstein1999normalized
breebaart2001a
gabor1946
lindemann1986a
dau1996qmeI
Adaptation
Models non-linear adaptation to the level of an input sound
adaptloop.m
demo_adaptloop.m
puschel1988pza
dau1996qmeI
breebaart2001a
Monaural models
Baumgartner model
Model for localization in saggital planes
baumgartner2013.m
plotbaumgartner2013.m
demo_baumgartner2013.m
exp_baumgartner2013.m
data_baumgartner2013.m
baumgartner2013assessment
baumgartner2012modelling
langendijk2002contribution
patterson1988efficient
dau1996qmeI
Dau model
Modulation filterbank and adaptation loop
dau1996preproc.m
dau1997preproc.m
dau1996qmeI
dau1996qmeII
dau1997mapI
dau1997mapII
Jepsen model
Computes non-linear internal representation of a signal
jepsen2008preproc.m jepsen2008cmh
Langendijk model
Localization model in the saggital plane
langendijk.m
likelilangendijk.m
plotlangendijk.m
plotlikelilangendijk.m
exp_langendijk2002.m
data_langendijk2002.m
langendijk2002contribution
Roenne model
Simulates ABR wave V latency and amplitude
roenne2012.m
roenne2012chirp.m
roenne2012click.m
roenne2012tonebursts.m
plotroenne2012.m
plotroenne2012chirp.m
plotroenne2012tonebursts.m
exp_roenne2012.m
data_roenne2012.m
data_elberling2010.m
data_neely1988.m
roenne2012_elberling2010stim.mat
roenne2012_harte2009stim.mat
roenne2012modeling
elberling2010evaluating
zilany2007representation
Viemeister model
Leaky-integrator model
viemeister79.m ???
Zilany model Humanized auditory nerve model zilany2007humanized.m roenne2012modeling
zilany2007representation
Binaural models
Breebart model
Computes the EI-cell representation of an input signal
breebart2001preproc.m
eicell.m
breebaart2001binaural
Dietz model
Binaural localization model using a count-comparison model to calculate the interaural phase difference.
dietz2011.m
dietz2011interauralfunctions.m
demo_dietz.m
exp_dietz2011.m
data_dietz2011.m
dietz2011auditory
Lindemann model
Binaural localization model using a delay line with contralateral inhibition.
lindemann1986a.m
plotlindemann1986a.m
lindemannbincorr.m
lindcentroid.m
demo_lindemann1986a.m
exp_lindemann1986.m
lindemann1986a
lindemann1986b
gaik1993combined
jeffress1948place
hess2007phd
May model
GMM-based localization model
may2011.m
may2011neuraltransduction.m
demo_may2011.m
may2011
Taakanen model
Physiological motivated localization model using the count-comparison mechanism
takanen2013.m (missing) takanen2013contracomparison.m
takanen2013cueconsistency.m
takanen2013directionmapping.m
takanen2013formbinauralactivitymap.m
takanen2013lso.m
takanen2013mso.m
takanen2013onsetenhancement.m
takanen2013periphery.m
takanen2013wbmso.m
demo_takanen2013.m
exp_takanen2013.m
data_takanen2013.m
takanen2013
Wierstorf model
Estimate the perceived direction and error of a virtual source for Wave Field Synthesis
wierstorf2013.m
wierstorf2013itd2anglelookup.mat
download_hrtf.m
estimate_azimuth.m
itd2angle.m
itd2anglelookuptable.m
exp_wierstorf2013.m
data_wierstorf.m
wierstorf2013
Ziegelwanger model
Time of arrival estimation for binaural impulse responses
ziegelwanger2013.m
ziegelwanger2013onaxis.m
ziegelwanger2013offaxis.m
plotziegelwanger2013.m
exp_ziegelwanger2013.m
data_ziegelwanger2013.m
ziegelwanger2013
Human data
Glasberg & Moore
Notched-noise data for the ERB scale
data_glasberg1990.m glasberg1990daf
moore1990auditory
Goode
...
data_goode1994.m goode1994nkf
Joergensen
...
data_joergensen2011.m joergensen2011predicting
Pralong
Head phone data (compensation ???)
data_pralong1996.m pralong1996role
SII weights siiweightings.m ???
Zwicker
Data for the Bark scale
data_zwicker1961.m zwicker1961saf
zwicker1999psychoacoustics