MCLACHLAN2021 - A dynamic ideal-observer model of human sound localization

Usage:

[results,template,target] = mclachlan2014(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)
itd0 : 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
itd0 : 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

est : estimated [num_sources, num_repetitions, 3]
real : actual [num_sources, 3]

params

additional model's data computed for estimations

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

Description:

mclachlan2021(...) is a dynamic 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 over a specified hear rotation. Parameters for the optimal Bayesian model are determined based on psychoacoustic discrimination experiments on interaural time difference and sound intensity.

MCLACHLAN2021 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]
`'dt',dt`	Time between each acoustic measurement in seconds. Default value is dt = 0.005.
`'sig_itd0',sig`	Set standard deviation for the noise on the initial itd. Default value is sig_itd0 = 0.569.
`'sig_itdi',sig`	Set standard deviation for the noise on the itd change per time step. Default value is sig_itdi = 1.
`'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_S = 3.5.
`'rot_type',type`	Set rotation type. Options are 'yaw', 'pitch' and 'roll'. Default value is 'yaw'.
`'rot_size',size`	Set rotation amount in degrees. Default value is rot_size = 0.
`'stim_dur',dur`	Set stimulus duration in seconds. Default value is stim_dur = 0.1.

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

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.