Acoular 24.10 documentation

Acoular – Acoustic testing and source mapping software

Main Page   ::   Installation  »

Acoular – Acoustic testing and source mapping software

Acoular is a framework for acoustic beamforming that is written in the Python programming language. It is aimed at applications in acoustic testing. Multichannel data recorded by a microphone array can be processed and analyzed in order to generate mappings of sound source distributions. The maps (acoustic photographs) can then be used to locate sources of interest and to characterize them using their spectra.

Three different point sources Airfoil leading edge noise Pantograph noise

A few highlights of the framework:

  • covers several beamforming algorithms

  • different advanced deconvolution algorithms

  • both time-domain and frequency-domain operation included

  • 3D mapping possible

  • application for stationary and for moving targets

  • supports both scripting and graphical user interface

  • efficient: intelligent caching, parallel computing with Numba

  • easily extendible and well documented

If you discover problems with the Acoular software, please report them using the issue tracker on GitHub. Please use the Acoular discussions forum for practical questions, discussions, and demos.

Our blog has detailed tutorials about how to set up and run an analysis and how to provide input data.

Contents:

Installation

Description of the different download and installation options to get Acoular running on your system.

Getting Started

The basics for using Acoular, explained with a simple example.

Examples

Example scripts covering different use cases.

What’s new

Release notes for the current version of Acoular.

Contributing

Information on how to contribute to the development of Acoular.

Reference Manual

All modules, classes and methods featured in Acoular are described in detail here. They can easily be browsed through an inheritance tree and cross links.

Literature

In here some of the publications used for this program package are listed. Further reading to fully understand how the algorithms work is recommended.

Indices and tables

Fork me on GitHub

Main Page   ::   Installation  »