RELANOIBORRA2019 - Modulation filterbank (based on DRNL)

Usage

out = relanoiborra2019(insig_clean, insig_noisy, fs, varargin)
out = relanoiborra2019(insig_clean, insig_noisy, fs, flow, fhigh, varargin)
[out, clean, noisy] = relanoiborra2019([..])

Input parameters

`insig_clean`	clean speech template signal
`insig_noisy`	noisy speech target signal
`fs`	Sampling frequency
`flow`	lowest center frequency of auditory filterbank
`fhigh`	highest center frequency of auditory filterbank
`N_org`	length of original sentence. Will be double the length of 'insig_clean' if not provided.
`sbj`	subject profile for drnl definition. default: 'NH'

Output parameters

out

correlation metric structure The out structure has the following fields:

.dint : correlation values for each modulation band
.dsegments : correlation values from each time window and mod. band
.dfinal : final (averaged) correlation

Description

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 Leve 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. J. Acoust. Soc. Am., 146(5), 2019.