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

Project description

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

Requirements

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

The following external libraries are required:

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 on nbviewer. 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.

View interactively on nbviewer

AE1_img

Exp2: Measured plane wave

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

View interactively on nbviewer

AE3_img

Exp4: Binaural rendering

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

View interactively on nbviewer

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
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 by Benjamin 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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