DATA_PAUSCH2022 - - Results from Pausch et al. (2022)

Usage:

[data,itd] = data_pausch2022(type_stf,type_mod,fig,plot)

Input parameters:

type_stf

flag that may be used to choose between one of the following:

hrtf load individual HRTF datasets
hartf_front load invidual front HARTF datasets
hartf_rear load invidual rear HARTF datasets

Output parameters:

data

data struct

(if type_stf=={'hrtf','hartf_front','hartf_rear'}) Individual spatial transfer functions, with fields id, the participant ID and sofa, the sofa file
(if weights=='weights') Polynomial regression weights (polynomial degree of P=4), to be applied on a subset of M=5 individual features
if type_mod=={'kuhn','woodworth','woodworth_ext'}: vector of (M*P+1) x 1 weights, if type_mod=='pausch': matrix of (M*P+1) x 4 weights
(if features=='features') Individual features as published in Pausch et al. (2022)

itd

itd struct (if type_stf=={'hrtf','hartf_front','hartf_rear'}) Estimated ITDs for directions in the horizontal plane

itd_hor: ITDs in s [double]
itd_max: maximum ITD in s [s]
itd_arg_max_idx: index of the argument of the the maximum ITD [double]
itd_arg_max_phi: argument of the the maximum ITD in deg [double]
itd_hor_mean: direction-dependent mean ITDs across participants in s [double]
itd_hor_std: direction-dependent standard deviation of ITDs across participants in s [double]
bp_fc_low: lower cut-off frequency (Hz) of the bandpass filter applied before estimating the ITDs [double]
bp_fc_high: upper cut-off frequency (Hz) of the bandpass filter applied before estimating the ITDs [double]

Description:

The type_mod flag may be used to select the ITD model for the ITD predictions (only required if weights=='weights'):

`'pausch'`	hybrid ITD model by Pausch et al. (2022) (default)
`'kuhn'`	analytic ITD model by Kuhn (1977)
`'woodworth'`	analytic ITD model by Woodworth and Schlosberg (1954)
`'woodworth_ext'`	analytic ITD model by Woodworth and Schlosberg (1954), extended by Aaronson and Hartmann (2014)

The weights flag may be one of the following:

`'no_weights'`	do not load polynomial regression weights (default)
`'weights'`	load polynomial regression weights

The features flag may be one of the following:

`'no_features'`	do not the individual features (default)
`'features'`	load the individual features

Additional key/value pairs include:

`'bp_fc_low'`	lower cut-off frequency (Hz) of the bandpass filter applied before estimating the ITDs (default: []) [double]
`'bp_fc_high'`	upper cut-off frequency (Hz) of the bandpass filter applied before estimating the ITDs (default: []) [double]

Requirements:

SOFA API v0.4.3 or higher from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)
Data in auxdata/pausch2022 (downloaded on the fly)

References:

F. Pausch, S. Doma, and J. Fels. Hybrid multi-harmonic model for the prediction of interaural time differences in individual behind-the-ear hearing-aid-related transfer functions. Acta Acust., 6:34, 2022. [ DOI | http ]

G. F. Kuhn. Model for the interaural time differences in the azimuthal plane. The Journal of the Acoustical Society of America, 62(1):157--167, 1977. [ DOI | arXiv | http ]

R. S. Woodworth and H. Schlosberg. Experimental psychology, Rev. ed. Holt, Oxford, England, 1954.

N. L. Aaronson and W. M. Hartmann. Testing, correcting, and extending the Woodworth model for interaural time difference. The Journal of the Acoustical Society of America, 135(2):817--823, 2014. [ DOI | arXiv | http ]