REIJNIERS2014 - Bayesian spherical sound localization model (basic)

Usage

[results,template,target] = reijniers2014(template,target,'num_exp',20,'sig_S',4.2);

Input parameters

`template`	struct, with the fields .fs sampling rate (Hz) .fc ERB frequency channels (Hz) .itd itd computed for each hrir (samples) .H Matrix containing absolute values of HRTFS for all grid points .coords Matrix containing cartesian coordinates of all grid points, normed to radius 1m .T angular template for each coordinate
`target`	struct, with the fields .fs sampling rate .fc ERB frequency channels .itd itd corresponding to source position .S sound source spectrum .H Matrix containing absolute values of HRTFS for all source directions .coords Matrix containing cartesian coordinates of all source positions to be estimated, normed to radius 1m .T angular template for each coordinate

Output parameters

`doa`	directions of arrival in spherical coordinates
`params`	additional model's data computed for estimations

Description

'doa' contains the following fields:

.est

estimated [num_sources, num_repetitions, 3] .real actual [num_sources, 3]

'params' contains the following fields:

.est_idx

: Indices corresponding to template direction where the maximum probability density for each source position is found .est_loglik : Log-likelihood of each estimated direction .post_prob : Maximum posterior probability density for each target source .freq_channels : number of auditory channels .T_template : Struct with template data elaborated by the model .T_target : Struct with target data elaborated by the model .Tidx : Helper with indexes to parse the features from T and X

reijniers2014 accepts the following optional parameters:

`'num_exp',num_exp`	Set the number of localization trials. Default is num_exp = 500.
`'SNR',SNR`	Set the signal to noise ratio corresponding to different sound source intensities. Default value is SNR = 75 [dB]
`'sig_itd',sig`	Set standard deviation for the noise on the itd. Default value is sig_itd = 0.569.
`'sig_I',sig`	Set standard deviation for the internal noise. Default value is sig_I = 3.5.
`'sig_S',sig`	Set standard deviation for the variation on the source spectrum. Default value is sig_I = 3.5.

Further, cache flags (see amt_cache) can be specified.

Description:

reijniers2014(...) is an ideal-observer model of human sound localization, by which we mean a model that performs optimal information processing within a Bayesian context. The model considers all available spatial information contained within the acoustic signals encoded by each ear. Parameters for the optimal Bayesian model are determined based on psychoacoustic discrimination experiments on interaural time difference and sound intensity.

Requirements:

SOFA API v1.1 or higher from http://sourceforge.net/projects/sofacoustics for Matlab (e.g. in thirdparty/SOFA)

References:

R. Barumerli, P. Majdak, R. Baumgartner, J. Reijniers, M. Geronazzo, and F. Avanzini. Predicting directional sound-localization of human listeners in both horizontal and vertical dimensions. In Audio Engineering Society Convention 148. Audio Engineering Society, 2020.

R. Barumerli, P. Majdak, R. Baumgartner, M. Geronazzo, and F. Avanzini. Evaluation of a human sound localization model based on bayesian inference. In Forum Acusticum, 2020.

J. Reijniers, D. Vanderleist, C. Jin, C. S., and H. Peremans. An ideal-observer model of human sound localization. Biological Cybernetics, 108:169--181, 2014.

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