BAUMGARTNER2021
Sound externalization based on multiple static cues

Usage:

[E,cues,cueLabels] = baumgartner2021( target,template )

Input parameters:

`target`	Binaural impulse responses referring to the directional transfer function(s) (DFTs) of the target sound(s). Option 1: given in SOFA format -> sagittal plane DTFs will be extracted internally. Option 2: binaural impulse responses of all available listener-specific DTFs of the sagittal plane with the size: (time x direction x channel).
`template`	Binaural impulse responses of all available listener-specific DTFs of the sagittal plane referring to the perceived lateral angle of the target sound. Format as target.

Output parameters:

`E`	predicted degree of externalization
`cues`	outcomes of individual cues
`cueLabels`	cue labels; cell array with 1st col. denoting acronyms and 2nd col. for descriptions

Description:

baumgartner2021(...) is a model framework for auditory externalization perception. It enables to probe the contribution of cue-specific expectation errors and to contrast dynamic versus static strategies for combining those errors within static listening environments.

baumgartner2021 accepts the following optional parameters:

`'cueWeights',cW`	Set the weights of individual cues to determine the final externalization score. Cue-specific weights (entered as a vector) are ordered as follows: 1 monaural spectral similarity (MSS) 2 interaural spectral similarity of ILDs (ISS) 3 spectral standard deviation of monaural gradients (MSSD) 4 spectral standard deviation of ILDs (ISSD) 5 interaural broadband time-intensity coherence (ITIT) 6 interaural coherence (IC) 7 monaural intensity difference (MI) 8 temporal standard deviation of ILDs (ITSD). Default weights are 0.6 for MSS, 0.4 for ISS, and 0 for all others.
`'S',S`	Set the cue-specific sensitivity parameter to S. 1/S represents the slope of sigmoidal mapping function. Vector order equivalent to cueWeights. Default values are determined by the weighted average sensitivities determined in Baumgartner and Majdak (2020) - run exp_baumgartner2021('tab2') to show them.
`'lat',lat`	Set the apparent lateral angle of the target sound to lat. Default value is 0 degree (median SP).
`'range',c1`	Set the range factor of the externalization scores to c1. Default value is 3.78 from Hassager et al. (2016).
`'offset',c2`	Set the offset of the externalization score to c2. Default value is 1 from Hassager et al. (2016).
`'ILD_JND',L`	Set the just noticeable ILD difference to L from the internal template. Default value is 1 (dB).
`'ITD_JND',T`	Set the just noticeable ITD difference to T from the internal template. Default value is 20e-6 (s).

baumgartner2021 accepts the following flags:

`'LTA'`	Looser-takes-all strategy: Model selects minimal predicted externalization scores across cues with weights larger than zero.
`'MTA'`	Median-takes-all strategy: Model selects median predicted externalization scores across cues with weights larger than zero.
`'WTA'`	Winner-takes-all strategy: Model selects maximal predicted externalization scores across cues with weights larger than zero.

Requirements:

SOFA API from http://sourceforge.net/projects/sofacoustics for Matlab (in e.g. thirdparty/SOFA)

Circular Statistics Toolbox from http://www.mathworks.com/matlabcentral/fileexchange/10676-circular-statistics-toolbox--directional-statistics-

References:

R. Baumgartner and P. Majdak. Decision making in auditory externalization perception: model predictions for static conditions. Acta Acustica, 5:59, 2021. Publisher: EDP Sciences. [ DOI ]

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