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
File/folder | Description |
---|---|
Figs_1_2.m |
Generates Figures 1 and 2 from the paper, demonstrating step-by-step application of the complete algorithm to a shoebox impulse response. |
Fig_6.m |
Generates Figure 6 in paper, illustrating changes in echo density by computing echo density curves on IRs for a series of shoeboxes with ceiling progressively slid open, and fitting the temporal echo density model proposed in the paper, implemented in the function curve_fitting() . |
core/ |
Main signal processing algorithms |
data/ |
Synthetic impulse response data usable with the scripts |
README.md |
This README file. |
CONTRIBUTING.md |
Guidelines for contributing to the sample. |
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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