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Analyze, visualize and process sound field data recorded by spherical microphone arrays.

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Project description

The sound_field_analysis toolbox (short: sfa) is a Python port of the Sound Field AnalysisToolbox (SOFiA) toolbox, originally by Benjamin Bernschütz [1]. The main goal of the sfatoolbox is to analyze, visualize and process sound field data recorded by spherical microphonearrays. Furthermore, various types of test-data may be generated to evaluate the implementedfunctions. It is an essential building block of ReTiSAR, an implementation of real timebinaural rendering of spherical microphone array data.

  1. Shallow Foundation Analysis (SoFA) software is a newly-developed free stand-alone program based on Matlab for the calculation of bearing capacity and settlements of shallow foundations.
  2. Pitilakis, 'SoFA: A matlab based educational software for shallow foundation analysis and design', Computer Application in Engineering Education, 25: pp.
  3. SOFA Statistics is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Requirements

We use Python 3.7 for development. Chances are that earlier version will work too but this is currently untested.

Deep Foundation

The following external libraries are required:

  • Jupyter (for running Notebooks locally)
  • Plotly (for plotting)

Installation

For performance and convenience reasons we highly recommend to use Conda (miniconda for simplicity) to manage your Python installation. Once installed, you can use the following steps to receive and use sfa, depending on your use case:

  1. From PyPI:

    Install into an existing environment (without example Jupyter Notebooks):pip install sound_field_analysis

  2. By cloning (or downloading) the repository and setting up a new environment:

    git clone https://github.com/AppliedAcousticsChalmers/sound_field_analysis-py.git

    cd sound_field_analysis-py/

    Create a new Conda environment from the specified requirements:conda env create --file environment.yml

    Activate the environment:source activate sfa

    Optional: Install additional requirements in case you want to locally run the Jupyter Notebooks with examples:conda env update --file environment_jupyter.yml

Documentation

Find the full documentation at https://appliedacousticschalmers.github.io/sound_field_analysis-py/.

Examples

The following examples are available as Jupyter notebooks, either statically on GitHub or interactively onnbviewer. You can of course also simply download the examples and run them locally!

Exp1: Ideal plane wave

Ideal unity plane wave simulation and 3D plot.

Exp2: Measured plane wave

A measured plane wave from AZ=180°, EL=90° in the anechoic chamber using a cardioid mic.

Exp4: Binaural rendering

Render a spherical microphone array impulse response measurement binaurally. The example shows examples for loadingmiro or SOFA files.

Version history

v2020.1.30
  • Update of README and PyPI package
v2019.11.6
  • Update of internal documentation and string formatting
v2019.8.15
  • Change of version number scheme to CalVer
  • Improvement of Exp4
  • Update of read_SOFA_file
  • Update of 2D plotting functions
  • Improvement of write_SSR_IRs
  • Improved environment setup for jupyter notebook
  • Update of miro_to_struct
2019-07-30 v0.9
  • Implement SOFA import
  • Update Exp4 to contain SOFA import
  • Delete obsolete Exp3
  • Add named tuple HRIRSignal
  • Implement cart2sph and sph2cart utility functions
  • Add conda environment file for convenient installation of required packages
2019-07-11 v0.8
  • Implement Spherical Harmonics coefficients tapering
  • Adaption of associated Spherical Head Filter
2019-06-17 v0.7
  • Implement Bandwidth Extension for Microphone Arrays (BEMA)
  • Edit read_miro_struct, named tuple ArraySignal and miro_to_struct.m to load center measurements
2019-06-11 v0.6
  • Port of Radial Filter Improvement from SOFiA

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2019-05-23 v0.5
  • Implement Spherical Head Filter
  • Implement Spherical Fourier Transform using pseudo-inverse
  • Extract real time capable Spatial Fourier Transform
  • Outsource reversed m index function (Exp4)

References

The sound_field_analysis toolbox is based on the Matlab/C++ Sound Field Analysis Toolbox (SOFiA) toolbox byBenjamin Bernschütz. For more information you may refer to the original publication:

[1] Bernschütz, B., Pörschmann, C., Spors, S., and Weinzierl, S. (2011). SOFiA Sound Field Analysis Toolbox.Proceedings of the ICSA International Conference on Spatial Audio

The Lebedev grid generation was adapted from an implementation by Richard P. Muller.

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2020.1.30

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