Applies to version: 1.1.0

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RELANOIBORRA2019 - Modulation filterbank (based on DRNL)


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


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.

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 f inal (averaged) correlation


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. [ www: ]

M. Jepsen, S. Ewert, and T. Dau. A computational model of human auditory signal processing and perception. J. Acoust. Soc. Am., 124(1), 2008. [ www: ]