These scripts and data illustrate the "Sorted Center Time" measure for temporal echo density in impulse response signals that is proposed in the following paper:

A Sparsity Measure for Echo Density Growth in General Environments,
Helena Peic Tukuljac, Ville Pulkki, Hannes Gamper, Keith Godin, Ivan Tashev, Nikunj Raghuvanshi,
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2019

Matlab scripts were tested on R2018a

• The algorithms here compute the echo density as an unnormalized percentage value that is 0 for a Dirac delta and 50 for a constant signal.
• Divide with the value obtained from gaussian_measure() to turn sorted-center-time into a normalized echo density that is 0 for Dirac impulse and 1 for Gaussian noise.

If you employ the data or algorithms, please cite using Bibtex key below.

@inproceedings{Tukuljac_EchoDensity:2019,
author={H. P. {Tukuljac} and V. {Pulkki} and H. {Gamper} and K. {Godin} and I. J. {Tashev} and N. {Raghuvanshi}},
booktitle={ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing},
title={A Sparsity Measure for Echo Density Growth in General Environments},
month={May},
year={2019},
volume={},
number={},
pages={},
doi={10.1109/ICASSP.2019.8682878},
ISSN={1520-6149},
keywords={acoustic analysis;architectural acoustics;echo;geometry;numerical analysis;statistical analysis;transient response;detailed temporal evolution;acoustic analysis;general environments;smooth sorted density measure;echo density growth;general power-law model;measured simulated impulse responses;numerically simulated impulse responses;growth power;sparsity measure;simple room geometries;complex scenes;statistical parameter;indoor response;outdoor response;Density measurement;Acoustic measurements;Microsoft Windows;Reverberation;Market research;Geometry;impulse response;echo density;mixing time;outdoor acoustics;parametric models;statistical signal processing},
}

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