Code for Numerical Experiments in "Efficient Forced Response Computations of Acoustical Systems with a State-Space Approach"

This repository contains code for the numerical experiments reported in

Art J. R. Pelling, Ennes Sarradj Efficient Forced Response Computations of Acoustical Systems with a State-Space Approach Acoustics 2021, 3, 581-593. doi:10.3390/acoustics3030037

The input data are taken from the Aachen Multi-Channel Impulse Response Database (MIRD)[1] and can be downloaded here.

It contains an implementation of randomized SVD as described in [2] on the basis of this repository. Our implementation is less verbose and faster. Furthermore, fast matrix-vector multiplications for block-Hankel matrices are enabled by providing a dedicated ndarray subclass.

Installation and Usage

The downloaded database archive has to be extracted into "./data/MIRD/" in order to be loaded correctly.

The required dependencies are listed in requirements.txt and can be installed into a new conda envrionment with

$ cd /path/to/repo
$ conda create -n <env> -f=requirements.txt

After these steps, the figures can be created by setting the appropriate parameters in script.py and executing

$ conda activate <env>
$ python script.py

References

[1] E. Hadad, F. Heese, P. Vary and S. Gannot, "Multichannel audio database in various acoustic environments", 2014 14th International Workshop on Acoustic Signal Enhancement (IWAENC), 2014, pp. 313-317, doi: 10.1109/IWAENC.2014.6954309.

[2] N. Halko, P. G. Martinsson, and J. A. Tropp, "Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions", SIAM Review 2011 53:2, 2011, 217-288, doi: 10.1137/090771806.