RELANOIBORRA2019_FEATUREEXTRACTION
Creates internal representation based on Relano-Iborra et al. (2019)

Usage:

[out, varargout] = relanoiborra2019_featureextraction(insig, fs, varargin)
[out, varargout] = relanoiborra2019_featureextraction(insig, fs, flow, fhigh, varargin)

Input parameters:

`insig`	signal to be processed
`fs`	Sampling frequency
`flow`	lowest center frequency of auditory filterbank
`fhigh`	highest center frequency of auditory filterbank
`sbj`	subject profile for drnl definition

Output parameters:

out

correlation metric structure inlcuding

Description:

The out struct contains the following fields:

.dint

correlation values for each modulation band

.dsegments

correlation values from each time window and mod. band.

.dfinal

final (averaged) correlation

This script builds the internal representations of the template and target signals according to the CASP model (see references). The code is based on previous versions of authors: Torsten Dau, Morten Loeve Jepsen, Boris Kowalesky and Peter L. Soendergaard

The model has been optimized to work with speech signals, and the preprocesing and variable names follow this principle. The model is also designed to work with broadband signals. In order to avoid undesired onset enhancements in the adaptation loops, the model expects to recive a prepaned signal to initialize them.

References:

H. Relaño-Iborra, J. Zaar, and T. Dau. A speech-based computational auditory signal processing and perception model. The Journal of the Acoustical Society of America, 146(5), 2019. [ DOI ]

M. Jepsen, S. Ewert, and T. Dau. A computational model of human auditory signal processing and perception. The Journal of the Acoustical Society of America, 124(1), 2008.