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Note that the table is currently outdated.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